Enhanced Algorithm and Comparative Study of Sales Forecasting Model Based on Data Mining Technology

Enhancement algorithms and comparative research play an important role in sales forecasting models, but there are problems of inaccurate forecasting models. Traditional deep learning cannot solve the enhancement and forecasting problems in the sales forecast model, and the prediction effect is not satisfactory. Therefore, this paper proposes an enhanced algorithm and comparative research on sales forecasting model based on data mining technology and analyzes the enhancement algorithm and comparison of sales forecasting model. Firstly, the decision tree theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the enhanced algorithm and comparative research, to reduce the interference factors in the reinforcement algorithm and comparative research. Then, the decision tree theory is used to form a data mining technology enhancement algorithm and a comparative research scheme, and the enhanced algorithm and comparative research results is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the data mining technology is superior to the traditional deep learning in terms of enhanced algorithm and comparative research accuracy, enhanced algorithm and comparative research influencing factor time.