The traveling salesman problem (TSP) is a classical NP-hard problem. The research on it has never stopped, and a lot of approximate solving algorithms have been obtained. However, each algorithm has its own characteristics, and thus new algorithms are proposed frequently for TSP, such as the sparrow algorithm developed recently. This work studies and analyzes the principle, search strategy, and basic process of the sparrow search algorithm (SSA). When the search by SSA approaches the global optimum, the diversity of the population decreases and it is easy to fall into the local optimum. Given this, the work proposes an improved sparrow search algorithm (ISSA). Six standard test functions, the basic SSA, and other swarm intelligence algorithms are employed in simulation experiments to test the performance of ISSA. Finally, ISSA is used to solve the TSP. Experiments show the effectiveness of ISSA in improving the shortcomings of SSA and enhancing the optimization ability and verify the feasibility and superiority of ISSA in TSP solving.