Recording and analyzing the data generated by online learners on the Internet and providing accurate and personalized services is an important aspect of online education. This study takes the daily learning data generated by learners on the teaching platform as a sample, synthesizes its five most representative influencing factors, classifies samples by Learning Vector Quantization (LVQ) neural network, and obtains online learning academic performance prediction data based on BP network. The genetic algorithm is used in the model to effectively optimize the weights and thresholds of the BP network, which accelerates the convergence of the model while improving the prediction accuracy. Finally, compared with the other two models, the results show that the model's prediction results are basically consistent with the real performance distribution. It has a high degree of credibility and provide a decision-making basis for effective prediction of learning status, which has certain value in engineering application.