The process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources, and the use of computer-aided prediction of the molecular properties of drugs can greatly save the time and cost of drug development. Therefore, in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ERα, for the collected 1974 compounds, the random forest classifier was first used to screen the top 20 molecular descriptions with the most significant effects on biological activity. The QSAR model is established using this and pIC50 value as characteristic data. Secondly, based on the PSO optimized BP neural network to predict the biological activity values of 50 new compounds, the model fit is 0.8337, and the root mean square error is 0.7315, which is more realistic than the predicted value of the BP neural network before optimization. Subsequently, in order to improve the success rate of drug development, the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data. The algorithm cross-validation CV accuracy rate reached 94.0767%, and the prediction accuracy rates of the five index models were all above 79%. The results show that the established model has better prediction performance than the benchmark model, and the adopted prediction strategy is effective, which can provide a reference for the research and development of anti-breast cancer drugs.