In order to quickly solve the optimal path of logistics transportation in a complex traffic environment, on the basis of traditional ant colony algorithm, a logistics transportation path optimization model based on improved ant colony algorithm is proposed. Firstly, the model adds constraints based on transportation time, cost and average road smoothness factor to the traditional ant colony algorithm, and improves the updating method of traditional pheromone to limit the maximum and minimum pheromone concentration on the road, so as to change the transfer probability of path selection. Finally, simulation experiments are carried out on the improved ant colony algorithm, CSAACO algorithm and ACO algorithm. Under the same experimental environment, the distance and time reduction of the three algorithms are tested. The experimental data show that the improved ant colony algorithm has significantly shorter transportation distance and less transportation time than CSAACO algorithm and ACO algorithm. The improved ant colony algorithm has stronger global optimization ability, faster convergence speed, less time, and shorter optimal path, and improves the transportation efficiency of the entire logistics industry.