Aiming at the subjectivity and limitation of traditional evaluation methods, this paper puts forward an objective evaluation method based on algorithm model. By integrating machine learning technologies such as Support Vector Machine (SVM) and Deep Neural Network (DNN), and adopting Stacking integrated learning strategy, a composite model is constructed to improve the prediction accuracy and generalization ability. The model integrates multi-dimensional information such as enterprise financial data, market share and technological innovation ability, and can comprehensively evaluate the competitiveness of enterprises in industrial economy. In the aspect of model input, several key indicators such as enterprise financial data, market share and technological innovation ability are considered to ensure the comprehensiveness and accuracy of evaluation. To verify the validity of the model, this paper selects high-tech industry as the case study object, collects relevant data of 10 enterprises, and carries out strict data preprocessing and model training. Through model prediction, the competitiveness score of each enterprise is obtained, and ranking and classification analysis are carried out. The results show that the model can effectively identify enterprises with strong competitiveness in the industry and provide targeted improvement suggestions. The research not only provides a new idea and method for quantitative evaluation of enterprise competitiveness, but also provides reference for evaluation in other industries. Through application of this model, enterprises can more accurately understand their position and advantages in the industrial economy, and provide strong support for strategic formulation and market competition.