Digital Signal Processing and Artificial Intelligence for Automatic Learning Xiaojuan Chen 3 3 2024 https://doi.org/10.6025/dspaial/2024/3/3/104-109 https://www.dline.info/dspai/fulltext/v3n3/dspaiv3n3_4.pdf Constructing an evaluation system for the practical effects of political education courses is beneficial for assessing existing educational work and serves as a “compass” for improving corresponding work. Therefore, this study explores the practical effects of college educational courses based on a computer artificial neural network model. The article first establishes a “three-dimensional index system” for evaluating educational courses in colleges based on the characteristics of artificial neural networks. Then, it introduces the computer artificial backpropagation (BP) neural network evaluation method and obtains strong empirical support through simulation experiments. Finally, a feasible “college artificial neural network evaluation model” is constructed. The simulation experiments show that the model’s actual output is very close to the expected results, and satisfactory results are achieved when testing the model with sample data. Using the BP neural network model effectively eliminates traditional evaluation methods, significantly increasing the implementation efficiency of the evaluation. The improvement of the BP neural network enhances its flexibility and performs excellently in training. The model’s expected performance in simulation experiments is highly consistent with actual performance, and it also achieves satisfactory results when measuring its performance using test data. In college education, it is a widely used evaluation method.