Intelligent Psychology Teaching System Based on Adaptive Neural Network

In order to study the intelligent psychology system, this paper proposes the role of adaptive neural network based on it and uses the ICAP learning method to compare with it. Firstly, the basic structure of the neural network in the teaching system is introduced, the psychological teaching algorithm based on the adaptive neural network is introduced, the ideas are formulated, and the four learning methods and the design elements of the adaptive neural network are described. The corresponding relationship between the four learning methods and the adaptive neural network is explained. The most popular and advanced adaptive neural network module usage statistics are made. The network model on the right is more advanced than the left, and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them, the number of interactive learning elements inception modules used by the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2 are 9, 10, 14, and 20, respectively. Inception-v4 also employs 2 interactive learning element reduction modules. Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules. The ICAP classification method is experimentally studied. The design of the experiment adopts passive method (P), active method (A), constructive method (C), and interactive method (I), respectively, to learn a short text in materials science. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly. With the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.