Narrow Results

The purpose of this paper is to control and judge the big data of students’ learning and living conditions in college education. College students’ book-borrowing data are mined deeply from three aspects, the multi-source preprocessing of students’ borrowing data from university and college libraries, the quantification of students’ book borrowing, and academic performance prediction by learning and book borrowing. The data mining technology analyzes and processes students’ primary information, score information, and book-borrowing information. Students’ book borrowing is modeled and analyzed using the backpropagation neural network (BPNN) algorithm, and the constructed BPNN book-borrowing model’s loss function is optimized based on the partial differential equations. The library access control data and book-borrowing data are used for statistics of the learning behavior frequency. Data such as students’ stay duration in the learning area and attendance rate are input into the analysis model for experiments, and the average absolute error, the mean square error, and the determination coefficient evaluate the prediction results. The results show that as students’ booking borrowing frequency decreases, their scores decrease, and students who often borrow books have strong learning motivations. In Experiment 4, when $R^2$ reaches its maximum value, 0.594, the predicted scores by the students’ book-borrowing model have a high correlation with students’ actual scores, indicating that the BPNN algorithm has the best prediction results. The results show that the indicator of students’ book borrowing has significantly improved the model’s prediction performance, and the borrowed book number and book-borrowing frequency are significant in the prediction model construction.

The purpose of this paper is to improve the financial management efficiency of large enterprises and enhance the overall operation vitality of enterprises. First, the connotation and characteristics of enterprise resource planning (ERP) are analyzed, and the financial ERP system is established. Then, the relevant dynamic models of nonlinear systems are classified and their characteristics are analyzed. Moreover, the system model of enterprise financial risk management is constructed based on the key success factors of project implementation risk and control flow chart of project life cycle. Finally, based on MATLAB software, $Z$ large enterprise is taken as an example to evaluate the implementation effect of analytical hierarchy process (AHP) algorithm and back propagation neural network (BPNN) algorithm in ERP system. The results reveal that compared with 2019, the capital concentration in 2020 increases by 8%, the operating cost decreases by 23.6%, and the expense reimbursement process time decreases from 60–80 days to about 6 days. The expected output and assessment result of AHP are 6.912 and 6.823, respectively, and the error between them is 0.0196. The expected output and assessment result of BPNN are 6.798 and 6.675, respectively, and the error between them is 0.0121. The error value of BPNN in ERP implementation effect assessment is less than that of AHP, which indicates that the assessment effect of BPNN is better than that of AHP.

The purpose is to further explore the application effect of the neural network algorithm in defense audit and improve the user information security performance. Based on the relevant theoretical basis of neural network in machine learning, the back propagation neural network (BPNN) algorithm model is constructed and optimized. Moreover, by comparing with the classification and prediction effect of the decision tree method, the application effect of BPNN is further clarified. Through statistical analysis, a total of six risk users are screened out. The test data are classified into non-risk user group and risk user group to study the prediction of classification. The specific results are as follows. The prediction accuracy of non-risk group is 99% by using the BPNN algorithm and that is improved to 99.5% by using the optimized BPNN; for risk group, the prediction accuracy of BPNN is only 50% and that of optimized BPNN is 83.3%. Meanwhile, the prediction error rate of the BPNN algorithm is significantly lower than that of the decision tree algorithm, which further verifies the good application effect of the BPNN algorithm. This study can provide scientific and effective reference for the follow-up research of defense audit.