Deep learning techniques are developing rapidly and are promising solutions for various problems. The rapidity of society has led to a common problem faced by companies in the logistics market: real-time logistics and distribution path optimization. This paper establishes a model for the logistics distribution path optimization problem, which aims to improve the traditional algorithm's optimization capability under complex urban road conditions. A detailed introduction is made to each part of the model, introducing weight updates to the ant algorithm for optimization, solving the unreasonable problem of the ant algorithm's setting of road parameters, and proposing the DBNTFPO algorithm. Relevant applications of deep learning technology are analysed to explore the relationship between deep learning technology and the real-time distribution of vehicle paths as an optimization problem. Finally, the challenges issued by the real-time logistics distribution path optimization problem to deep learning are drawn out using a decision support system. Finally, the effectiveness and feasibility of this paper's algorithm in practical logistics distribution are demonstrated through example analysis.