In response to the practical management needs for strengthening industrial risk monitoring capabilities,this approach enhances the analytical abilities of management departments and think tank researchers in analyzing enterprise technology risk issues within specific industries.It also provides support through machine learning models for a systematic understanding of technology risk activation or suppression.By defining enterprise risk and categorizing it into multiple dimensions,a machine learning-based method is utilized to construct an Enterprise Technology Risk Threshold Activation Mode,which aims to deeply explore the characteristics of enterprise risk.It employs eight machine learning algorithms,including Random Forest,XGBoost,etc.,to train parameter variables for learning the attributes of enterprise technology risk,as well as for evaluating the model's effectiveness.It reveals the regularity characteristics of corporate risk activation using automated methods in the case of the Intelligent Connected Vehicle(ICV)industrial chain,with the classification prediction accuracy of three gradient boosting synthesis models reaching 82.59%.This enables the identification of enterprises with potential technical risks from a large dataset of relevant variables.Future work will focus on further improving the prediction accuracy and stability of these models,as well as expanding their application in enterprise technology risk assessment across various fields.