Fecha de publicación: 24/11/2020 Fuente: Wipo "BigData" The invention discloses a multi-level deep fusion mining method for multi-mode cross-boundary big data of commercial vehicles. The method comprises the following steps: S1, collecting an original dataset of multi-mode cross-boundary big data of a vehicle; s2, performing data preprocessing on the collected original data set; s3, performing data mining on the preprocessed data by using a WEKA algorithm to extract feature keywords; s4, calculating the weights of the feature keywords and the similarity between the different feature keywords through a TF-IDF technology, and constructing a weight and similarity matrix; and S5, constructing a regression model based on the sample. According to the invention, via t-SNE dimension reduction, WEKA algorithm feature extraction and a TF-IDF algorithm,an analysis strategy of dimension reduction and feature extraction in sequence is adopted for high-dimensional data, effective fusion of multi-level deep fusion mining of cross-boundary big data is achieved, and the problems that due to a high-dimensional data set with complex data types and numerous data features, the fusion efficiency is low, and the working efficiency is not remarkably improvedare solved.