Objectives: Based on intelligent manufacturing technology, this paper studies the informatization method of green innovation reform of tobacco manufacturing enterprises in the era of big data. Methods: By combing the literature at home and abroad, this paper expounds the technologies of data mining and data visualization based on the idea of lean management. On this basis, the basis of data management in the silk workshop of X tobacco enterprise is investigated and analyzed in detail from the aspects of human resources, equipment and management environment. Taking the blade feeder in the silk workshop of X cigarette factory as the research object, this paper establishes the self inspection model of equipment health state by using the weighted sum method of overall linearity and local nonlinearity. Results: The system can self check the health status of equipment and visually display the results. This method realizes timely and effective inspection and early warning of equipment health status. At the same time, the visual Kanban of workshop site management can provide production and equipment information to relevant personnel in time. This meets its demand for data visualization and changes the current situation of workshop data table display. Conclusion: This method reengineers the information business process of tobacco enterprises and establishes a new performance evaluation system. This effectively improves the existing management system of the workshop.

This paper studies the competitiveness of listed companies in high-end equipment manufacturing industry by using random forest. Random forest is a supervised machine learning algorithm that is actually based on the regression and classification. It takes some important decisions that are always based upon the set of samples. It counts majority for the classification purposes while it takes an average for the regression. For empirical analysis, 88 listed companies are selected. It is found that there are great differences in comprehensive competitiveness among industries. Enterprise scale accounts for a high proportion in the comprehensive competitiveness, and its score often affects the comprehensive strength; and the gap between companies in the same industry is also obvious. The empirical evaluation results of this paper provide three enlightenments for enterprises to improve their comprehensive competitiveness, such as seizing the strategic opportunity to expand the market, expand the scale of enterprises, improve asset management, and narrow the industry gap.