<p>Predicting the patent value is an essential component of corporate strategic planning. We proposed a patent value index system consisting 17 features, utilizing forward citations as a proxy for patent value. We then constructed machine learning models to predict the patent value of 108,781 patents in the low emission vehicle (LEV) field. Finally, we evaluated the impact of different features on prediction performance and assess the accuracy of patent value classification. Our experiments show that BPNN outperforms five other baseline models on the test set. We identified 9 features that significantly affect the patent value prediction, including abstract length, patent novelty, 4-digit IPCs, CPCs, family count, claim length, assignee capabilities and scientific knowledge. By predicting patent value, this study can assist companies in identifying technological gaps and optimizing resource allocation.</p>