To solve the problem of uncertain time series (UTS) motif discovery (MD), a motif discovery algorithm for UTS based on particle swarm optimization (PSO) is proposed. According to the characteristics of UTS, a study framework based on PSO for MD from UTS is designed. Furthermore, through coding and revising the start time of time series segment and the last time for it, the proposed algorithm can be realized to discover the motifs from the UTS. In the experiment, a real-life application is applied to verify the feasibility of the proposed algorithm. Then, it is compared with MK and MOEN in terms of run time and memory usage. Finally, its convergence and accuracy are analyzed. The results show that the proposed algorithm can be used to discover motifs with different lengths by consuming less runtime and memory usage, and it has convergence and high accuracy.