<p>In the current era of digitalization and globalization, innovation capability has become a key factor in corporate competition and sustainable development. This study analyzes the predictive factors of corporate innovation capability based on data from A-share listed companies between 2008 and 2022, utilizing multiple machine learning models. The results show that ensemble learning methods, such as Random Forest and CatBoost, outperform traditional linear regression models in predicting innovation capability, especially when handling high-dimensional data and nonlinear relationships. By gradually introducing company characteristics, it is found that governance factors and tax policies contribute the most to the prediction of innovation capability, particularly validated in ensemble learning models. The influence of financing and lifecycle factors is relatively limited, while digital transformation, R&amp;D investment, and executive characteristics enhance the model’s predictive performance. The comprehensive model, incorporating all features, achieves optimization in most algorithms, with particularly outstanding performance in the CatBoost and Random Forest models. Feature importance analysis reveals that factors such as executive shareholding ratio, career background heterogeneity, and digital transformation exhibit nonlinear effects on innovation capability. Moreover, moderate resource pressure and competition promote innovation, while excessive financing constraints and industry competition may suppress innovation potential. This study provides important references for companies to optimize resource allocation and enhance innovation capability, while offering data-driven support for policy formulation.</p>