The temperature of Lanzhou City is a non-stationary sequence with typical characteristics of loud noise and instability. As the temperature changes greatly, the sequence gets unstable. In order to improve the prediction accuracy, strengthen the generalization ability, and reduce the sensitivity of parameter selection of support vector machine (SVM) in temperature prediction, in this study, the improved particle swarm optimization (IPSO) algorithm is proposed to optimize the temperature prediction model of SVM. Firstly, the adaptive inertia weight is introduced into the particle swarm optimization (PSO) algorithm to improve the global optimization ability and local development ability of the PSO algorithm. Secondly, the improved IPSO is used to optimize the penalty factor and kernel function parameter of the SVM, and the optimized model (IPSO-SVM) is applied to the temperature prediction. The actual data of Lanzhou ground observation station are taken as sample data, and Matlab experimental tools are used for training and prediction. The experimental results show that the IPSO-SVM model in this study has a stronger generalization ability and better fitting degree than back propagation (BP), SVM, GRID-SVM, GWO-SVM, ABC-SVM, and ACO-SVM. It can predict the change in temperatures more accurately, which further verifies the feasibility of this model in temperature prediction.