A Quality Prediction Method for Optimizing the Teaching Mode of History Courses Based on Multi-Category Data Fusion Analysis

In the process of predicting the optimization quality of teaching mode, a single convolutional neural network method is affected by multiple sources of data such as students’ behavioral data and teachers’ evaluations, which is prone to causing modal collapse and affecting the prediction quality. For this reason, we propose a method for predicting the optimization quality of history course teaching mode based on the fusion analysis of multi-class data. After collecting the data of optimizing the quality of teaching mode of historical courses, noise reduction is carried out by the noise reduction self-coder network on the data collection, and the combination of historical data and current data is realized by the dynamic slicing method of multi-feature matrix, after fusing the multi-category data, the data are inputted into the convolutional neural network, and an optimized multi-objective genetic algorithm optimizes the convolutional neural network, stabilizes the convolutional neural network model, and avoids the modal collapse caused by multi-source attributes and categories of the data. The optimized convolutional neural network is used to extract the deep features of the optimized quality of history course teaching mode, and the obtained features are input into the multiple linear regression model as independent variables to obtain the predicted scores of the optimized quality of history course teaching mode. Through experimental verification, the method can realize in-depth cleaning and denoising of the data of each feature of history curriculum teaching mode, and there is a large correlation between the features related to the optimization quality of the five history curriculum teaching modes collected by the method and the final score of the optimization quality of the history curriculum teaching modes, the method predicts that the optimization quality of the history curriculum teaching modes scores and the scores of the expert evaluation are basically consistent.