The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning.

This study aims to effectively improve the quality evaluation system of engineering practice teaching in colleges and universities and enhance the efficiency of teaching management. A brand-new teaching evaluation model is constructed based on the Internet of Things (IoT) technology, combined with complex network analytic hierarchy process and deep learning method. Firstly, with the help of open online course data, Natural Language Processing (NLP) technology and Generative Adversarial Network (GAN) algorithm are used to extract discipline-related features from the course content, and the data of 500 students in 10 majors are simulated and generated. Then, the real university curriculum content, teaching resources, and virtual student data are organically integrated, and two deep learning algorithms, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are introduced. RNN is used to capture time series information, and CNN is used to extract spatial features. Through the hierarchical analysis of complex network, the relationship between different teaching elements is revealed and the hierarchical structure is constructed. Meanwhile, dynamic characteristics are introduced, and continuous model updating and adaptation are realized by randomly combining data to adapt to the changes of actual educational environment. After the course training, data indicators such as students' homework, projects and exams are comprehensively extracted, and the correlation analysis between students' performance and characteristics, time series analysis, feature fusion and weight analysis, model performance evaluation and prediction analysis are carried out. Through the correlation analysis between students' performance and characteristics, the important characteristics that affect learning results are excavated. Time series analysis reveals the changing trend of learning process and better grasps students' learning state. Feature fusion and weight analysis comprehensively consider multiple key features to quantify students' comprehensive performance under different parameter characteristics. Model performance evaluation and prediction analysis compare the prediction results of the model with the actual performance to evaluate the accuracy and stability of the model. The results show that there is a positive correlation between curriculum dependence and interdisciplinary impact index (r = 0.725). The performance of student 3 is relatively stable, with the highest score of 91, and the score of students 7 fluctuates the most, from the lowest 47.9 to the highest 50.2. CNN characteristic index and RNN characteristic index are between 0.18 and 0.78. The comprehensive accuracy of the model in predicting students' actual grades reaches 76-98%, and the prediction consistency varies from 76 to 98%. This study aims to help reveal the relationship between students' performance and teaching evaluation factors, deepen the understanding of the evaluation model of engineering practice teaching in colleges and universities, and provide valuable guidance for optimizing teaching.