Abstract:Industrial enterprises have accumulated a large amount of production data. Massive industrial data contain valuable information. By analyzing and mining these industrial data, enterprises can enhance the ability of digital management and quality data analysis. This paper analyzes the demand and data characteristics of big data in tyre industry. First, the multi-source and heterogeneous data in every link of tyre production is integrated. After analyzing the data pre-processing process, we build the analysis data set of structured manufacturing and quality inspection. According to the low performance of the traditional ADTree algorithm, this study uses bottom induction method to make full use of the known data and reduce the amount of calculation. The experiment shows that the improved algorithm is more suitable for a large amount of data. After sorting out the results of ADTree, the important factors that affect the quality of the tires can be found.