Machine learning models provide new technical support for the protection of intellectual property rights. The study explores the current application of machine learning models in the field of intellectual property protection and constructs an evaluation index system for intellectual property. Then the training samples are selected and processed, the BP neural network model is constructed, and the genetic algorithm is used to optimize its initial weights and thresholds, and the GA-BP neural network prediction model is designed to evaluate the protection status of intellectual property. In the case study section, the evaluation of the legal intellectual property rights as an example is used to verify the validity, and the results show that the evaluation accuracy of the GA-BP neural network model reaches 93%, and the error is controlled within 10%, which provides a practical and operable objective evaluation method for the future evaluation of the value of legal intellectual property rights.