With abundant resources consumed by innovation practices, it is of strategic importance for industrial firms to identify key determinants of innovation performance from both internal and external environments for better and effective innovation management. To this end, this article develops a new machine learning-based approach to examine a variety of factors influencing innovation performance. In particular, a two-process modeling approach is developed to build machine learning models efficiently for identifying key internal and external innovation factors. With extensive experiments using data collected from China's pharmaceutical industry, the approach identifies a set of key internal and external innovation factors and their best configurations in terms of two commonly used innovation performance measures. The findings enable pharmaceutical firms and local governments in China to manage innovation efforts and formulate management strategies for promoting industrial innovation. The article contributes to innovation management research by providing an objective and data-oriented approach for comprehensively exploring internal and external innovation factors and establishing their relationships with expected innovation performance. Although the approach is exemplified by China's pharmaceutical industry and the results cannot be generalized, it has general applicability to other industries or countries.