In order to improve the vehicle loading rate and make full use of resources in vehicle transportation, this paper proposes a vehicle-cargo matching method based on view similarity in the way of the CBR system thinking. Firstly, the information of goods and vehicles is represented by the knowledge expression system, and the preliminary classification and matching of the two are realized based on the CR attribute of the vehicle and the N attribute of the goods; then, K-Means clustering is performed on the vehicle data set, and the Mahalanobis distance is used to calculate and match the information. Determine the cluster closest to the goods to be matched, and realize the lateral compression of the view matching space; finally, integrate and improve the traditional view calculation method, and use the Euclidean distance to calculate the view similarity between the goods to be matched and each vehicle in the given cluster, and submit the vehicle corresponding to the minimum view similarity. By crawling the Yunmanman platform data for experimental analysis, it is proved that the proposed method can significantly improve the loading rate of truck-cargo matching, and the matching efficiency is increased by about 76%.