The complex indoor environment is easily affected by the multipath effect and nonlineofsight, which leads to the unreliable RSSI value and affects the prediction performance of SVR model and positioning accuracy of the system. To solve the problem, a SVRPSO algorithm based on error correction and adaptive operator is proposed. This algorithm proposes to use the prediction error of the nearest neighbor reference labels to correct the prediction distance of the measured label, so as to make up for the inaccurate prediction of SVR model due to the unreliable RSSI value. Then, the nonlinear equations of the measured label’s position coordinates is constructed and solved iteratively by PSO algorithm. Aiming at the problem that the standard PSO algorithm is easy to fall into local optimum and the convergence speed is slow, an adaptive operator is designed to improve the inertia weight and learning factor of PSO algorithm respectively. The simulation results show that both error correction and adaptive operator have certain effects on improving the indoor positioning accuracy. Compared with SVRPSO, the average positioning accuracy of the system is improved by 316%. With the same positioning accuracy, the algorithm uses fewer reference tags.