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The application of multimedia technology in physical education classroom teaching has become increasingly widespread. It not only enriches teaching methods and makes the classroom more lively and interesting, but also enhances students’ learning interest and participation. Through multimedia technology, teachers can more intuitively display the essentials of actions, helping students better understand and learn. In addition, multimedia technology can also provide rich information resources to help students understand more sports knowledge and improve their overall quality. This paper introduces the basic concepts and characteristics of multimedia technology, including its interactivity, integration and innovation. Then, the application status of multimedia technology in physical education is deeply analyzed, and the main problems in teaching practice are revealed, such as the lack of technical equipment, the lack of teachers’ technical ability and the lack of teaching resources. Then, from three aspects of teaching content, teaching method and teaching effect, the influence of multimedia technology on PE classroom teaching quality is analyzed in detail. Physical education teachers should actively guide students to participate in multimedia learning, such as through interactive games, online discussions, etc., to stimulate students’ interest and initiative in learning, and improve learning effectiveness. Physical education teachers need to continuously learn and improve their multimedia technology application abilities, and master the latest teaching techniques and tools, in order to better integrate multimedia technology into classroom teaching. Schools can establish a multimedia teaching resource library to centrally manage excellent multimedia teaching resources, facilitate teachers to search and use, and improve teaching efficiency. Including strengthening equipment investment, improving teachers’ technical ability and developing high quality teaching resources.

Intelligent algorithms based on bionics have a natural advantage in solving nonlinear problems. With the development of computer technology, the development of related technologies has also been greatly promoted, so it has been widely used in a variety of fields. This paper first introduces the research status of the biome algorithm and some problems in the construction of the physical education structure. Through the analysis of the problems, the matrix model is a widely used method to analyze the dynamic changes in plant and animal populations. In this method, matrix algebra is used to describe the structure and change law of population, and the matrix operation model is established to simulate the process of population growth, migration and extinction. The matrix model method can take into account the interaction between individuals in the population and the influence of the external environment and can simulate the dynamic change of the population well. Completed the research on the construction of physical education structure.

To achieve the popularization of more innovative teaching modes and promote the rapid development of intelligent classroom teaching technology, it is necessary to design and develop the education and training platform more deeply, integrate with new teaching methods, and achieve an efficient teaching environment. We propose a load-balancing strategy based on deep reinforcement learning considering the problem of classroom terminal load. Based on the embedded classroom teaching platform, in the communication process, this strategy will let the reinforcement learning module model analyze the network. Then, through the advantages of the SDN global view, it obtains the status and information of the whole network, realizing the classroom interaction’s learning process. Optimizing the teaching structure is closer to the key and difficult points of teaching. The fuzzy perception clustering model is designed to improve teaching quality. By the embedded system, we process the data of the fuzzy perception model. Then, we combine fuzzy logic reasoning with the classroom teaching evaluation and utilize the fuzzy neural network to analyze the teaching situation. By training the network to get the optimal migration action decision, it can not only effectively improve the load-balancing effect of multiple controllers, but also reduce the balancing time and enhance the overall performance and stability of the network. Through the experimental analysis, compared with the traditional fuzzy neural network method, the mapping effect of the fuzzy clustering perception classification activation function used in this paper on the output of the normalized layer is verified. The clustering effect is 15.22% higher than that of the PE method and 9.32% higher than that of the SHKwon method. Besides, the improved algorithm improves the prediction rate of the network operation situation awareness. It is especially suitable for embedded systems with limited computing power.

Classroom teaching evaluation is one of the important contents of the new round of basic education curriculum reform in China. The new curriculum reform puts forward new requirements for the construction of the teaching evaluation system: promoting the all-round development of students, promoting the continuous improvement of teachers’ level, and promoting the curriculum of continuous development. However, from the current situation of the implementation of the new curriculum, the original teaching evaluation system is far from the requirements of the new curriculum reform, and does not have much practical value, and cannot provide strong support for the new curriculum reform. If it is not reformed, it will inevitably have a negative impact on the overall promotion of curriculum reform. How to improve classroom teaching evaluation under the background of the new curriculum reform, and how to establish a teaching evaluation scale and system suitable for the new curriculum reform, so as to play the role of evaluation in guiding, motivating and promoting, is an urgent problem to be solved at present. By referring to the relevant literature, the concepts of evaluation, teaching evaluation and classroom teaching evaluation are defined and discussed, and the object of classroom teaching evaluation is clarified.

Education refers to ideologies, traditions, culture, and values that guide education to economics, politics, morals, religions, information, reality, comparative and historical aesthetic, and artistic school knowledge. The challenging characteristics in political education include lack in knowledge sharing, user’s interactive experience, and incentive mechanism has become an essential factor. In this paper, the Deep Learning-Based Innovative Ideological Behavior Education Model (DL-IIBEM) has been proposed to strengthen the mechanism to promote information exchange, enhance the user’s interactive experience, and make the platform perform efficiently. Knowledge Network Mechanism Analysis is integrated with DL-IIBEM to strengthen user feedback probability, the average probability of completing social media tasks on a popular network, and the predicted utility degree for individual users. The entire platform is dramatically improved. The simulation analysis is performed based on the performance ratio based on data set 1 (98.2%) and 2 (95.3%), skill development ratio (95.3%), accuracy ratio, the teaching methods in ideological and political education, and Students Achievements ratio (98.2%) prove the proposed framework’s reliability.

University students face immense challenges in current situations in ideological and political research. Therefore, the way ideological work constantly needs to be adapted, and the exchange of advanced experience strengthened to increase ideological and political education (IPE) in universities. Specific methods of university administration may include only ideological and political courses. Courses information and student grades did not conduct an ideological or political evaluation of the student. They assessed the psychological behaviors of the student based on their success, nor did them include clear information on the course schedule for specific ideological and political courses. This article, Supervised learning-based teaching evaluation approach (SL-TEA), has been proposed to focus on supervised learning from ideas about machine learning technology and the current IPE status, to be developed using a brief analysis procedure. Supervised learning uses a practice set to provide the necessary quality through teaching models. Inputs and correct outputs that allow the model to learn over time are part of this training data. The study uses the system of experts to manage, operate and monitor model evaluation data and create a related database for a real-time update. Besides, to check the impact of the model and to run simulation tests. This study SL-TEA model follows the real needs of the system that the ideological and political teaching content of colleges and colleges can be evaluated. Thus, the experimental results show the better performance through the highest student accuracy ratio of 97.1 %, a high-performance ratio of 94.3%, improved political thinking rate of 92.8%, improved actual positive rate of 90.2%, the false-positive rate of 92.2%, enhance learning rate of 96.6% and reduce the error rate 21.2%, compared to other methods.

Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality.