Patent valuation is required to revitalize patent transactions, but calculating a reasonable value that consumers and suppliers could satisfy is difficult. When machine learning is used, a quantitative evaluation based on a large volume of data is possible, and evaluation can be conducted quickly and inexpensively, contributing to the activation of patent transactions. However, due to patent characteristics, securing the necessary training data is challenging because most patents are traded privately to prevent technical information leaks. In this study, the derived marketable value of a patent through event study is used for patent value evaluation, matching it with the semantic information from the patent calculated using latent Dirichlet allocation (LDA)-based topic modeling. In addition, an ensemble learning methodology that combines the predicted values of multiple predictive models was used to determine the prediction stability. Base learners with high predictive power for each fold were different, but the ensemble model that was trained on the base learners' predicted values exceeded the predictive power of the individual models. The Wilcoxon rank-sum test indicated that the superiority of the accuracy of the ensemble model was statistically significant at the 95% significance level.