This paper aims to optimize the vehicle-cargo matching of the logistics platform through the hybrid recommendation algorithm to achieve the effective matching between vehicle and cargo sources. This paper constructs a hybrid recommendation model taken into account the content-based recommendation algorithm and the item-based collaborative filtering algorithm. The content-based recommendation algorithm has advantages in matching the inherent attributes of vehicle and cargo sources, whereas the item-based collaborative filtering can deal with the problem of interest drift, and the advantages of these two algorithms are integrated in the hybrid model in this paper. The matching factors are pick out from the data of current logistics platforms, and the feedback competition method is applied for the calculation of the weights, and then the weight similarity is worked out for the recommendation. Multiple aspects are considered in the hybrid model, so as to satisfy the users by recommendation results, thereby achieving vehicle-cargo matching. This paper focuses on the matching of vehicles and cargoes in multiple dimensions, where the personalized and diversified comprehensive recommendation results are acquired to meet the needs of car owners and cargo owners.