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What kind of people to train, how to train them and for whom to train are the fundamental issues and eternal themes of education. The rapid advancement of artificial intelligence (AI) technology in recent years, as exemplified by deep learning and knowledge graphs, has opened up new avenues for innovation in education and modifications to teaching strategies. One of the most crucial subjects in the world of education nowadays is how to employ intelligent technology to encourage students to study intelligently. In the age of AI, smart learning is the fundamental meaning of education. The construction of smart learning models is the key and foundation for implementing smart learning, and it is also a bottleneck issue in research in this field. For the problem that it is difficult to characterize the intrinsic mechanism of intelligent learning, we propose the E-GPPE-C model of intelligent learning by utilizing AI technology, which can explain the operation mechanism, elements and characteristics of intelligent learning. Learning environment, learning route, learning assessment, learner image, educational knowledge map, as well as learning community make up the model. The base layer, service layer, support layer, application layer and key layer are all included in the model at the same time. We suggested the implementation approach of E-GPPE-C model from four perspectives: learning path suggestion, learner picture construction, learning community construction, and educational knowledge graph construction. These methods are based on algorithms linked to AI. The findings of this study lay the groundwork for the development of smart learning and the use of AI in the field of education, and provide a reference for subsequent research on smart learning models.

In recent years, with the growth of China’s economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people’s lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide.

Aiming to solve the problem of tracking drift during movement, which was caused by the lack of discriminability of the feature information and the failure of a fixed template to adapt to the change of object appearance, the paper proposes an object tracking algorithm combining attention mechanism and correlation filter theory based on the framework of full convolutional Siamese neural networks. Firstly, the apparent information is processed by using the attention mechanism thought, where the object and search area features are optimized according to the spatial attention and channel attention module. At the same time, the cross-attention module is introduced to process the template branch and search area branch, respectively, which makes full use of the diversified context information of the search area. Then, the background perception correlation filter model with scale adaptation and learning rate adjustment is adopted into the model construction, using as a layer in the network model to realize the object template update. Finally, the optimal object location is determined according to the confidence map with similarity calculation. Experimental results show that the designed method in the paper can promote the object tracking performance under various challenging environments effectively; the success rate increases by 16.2%, and the accuracy rate increases by 16%.

The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.

With the continuous development of the national economy, the social demand for electricity continues to increase, and the scale of investment in the power grid is also increasing. However, in recent years, the growth rate of electricity sales has slowed down due to the influence of national macro-control and industrial structure adjustment. In order to solve the problems faced by power companies in power investment management, this paper uses artificial intelligence technology to build a power company’s investment capability model. After summarizing the factors that affect the company’s investment, the calculation method of the relevant investment capacity of the power company is put forward. Through the application of the investment capacity model of the power company, it is estimated that the fixed assets will increase by 31.5 billion yuan, 31.1 billion yuan, and 32.4 billion yuan, respectively, in the next three years., totaling 95 billion yuan. After deducting inventory depreciation and new assets, the investment in the next three years will affect the company’s net increase in fixed assets of 11.7 billion yuan, 9.2 billion yuan, and 8.2 billion yuan, totaling 29.1 billion yuan, the audited investment scale in the next three years will be 41.7 billion yuan, 41.3 billion yuan, and 43 billion yuan respectively, totaling 1,260 yuan. The construction and application of this model are beneficial for power enterprises to optimize the allocation of limited resources and achieve precise and lean investment management.