Listed companies’violations have been an important issue that attracts the attention of the capital market． While studying the causal relationship between single-dimensional variables and this issue is crucial，constructing an effective holistic prediction model is also of great significance． This paper constructs a prediction model of listed companies’violations based on important company characteristics and managerial individual characteristics from the perspective of internal governance． Using a sample of Chinese A-share listed companies from 2008 to 2019，this study introduces two machine learning algorithms，LightGBM and SHAP，to examine the predictive ability，importance ranking，and prediction mode of the two types of characteristics on violation behaviors． The results show that the model can predict corporate violations to a certain extent，and corporate characteristics have a greater impact on the prediction than managerial individual characteristics． Specifically，higher information transparency of listed companies，higher net profit margin of total assets，lower asset-liability ratio，higher managerial shareholding ratio，lower performance volatility，and higher analyst attention are associated with a lower tendency for the model to predict violations． In addition，the model predicts an increased tendency for violations when executives are young and when the chairman and CEO roles are combined． Moreover，most corporate characteristics and managerial individual characteristics exhibit a non-linear relationship in predicting corporate violations，which is consistent with the findings of traditional theoretical and empirical studies． Overall，our study enriches the research on the characteristics of corporate executives in China from a predictive perspective and provides empirical evidence for regulatory authorities and investors to improve supervision and investment efficiency and for companies to optimize internal governance mechanisms．