The localization problem of wireless sensor network can be transformed into a fitness function optimization problem, which is solved by the classical sparrow search algorithm. However, the fitness function used in this algorithm does not use measured distance data between unknown nodes, resulting in limited improvement in positioning accuracy. To address this issue, a cooperative localization algorithm based on sparrow search is proposed. This algorithm mainly includes two search stages: rough search and fine search. In the rough search stage, the measured distance data between the unknown node and the anchor node is used to determine the initial position of the unknown node. In the fine search stage, the measured distance data between unknown nodes is used to determine the precise position of the unknown node. Firstly, the Cat chaotic mapping method is used to ensure the uniform distribution of the initial population, which helps to determine the optimal location. Secondly, two different fitness functions are constructed, one for rough search and the other for fine search. Among them, the fitness function used for fine search utilizes the measured distance data between unknown nodes to improve positioning accuracy. Finally, a new fine search method is proposed to avoid the convergence of cooperative localization results to the local optimal position. The effectiveness of the proposed method is verified through analysis of simulation and measured data.