Data in the process industry are highly time-varying and nonlinear. Traditional offline models can hardly cope with the changing working conditions in the actual production process, while the just-in-time learning ( JITL) is an effective online modeling method. Most of the studied similarity measurements of JITL only focus on samples’ spatial distance, which ignore the time-series characteristics of industrial data. To address this issue, a JITL method based on spatial-temporal similarity is proposed. First, the sample point is extended into a sample sequence, and the temporal-sequence distance among samples is calculated by combining dynamic time warping. Then, the spatial-temporal similarity metric (SSM) is proposed, and the SSM is constructed by nonlinearly weighting the temporal and spatial distances. Finally, the online modeling method for just-in-time learning based on spatial-temporal similarity ( SSJITL) is proposed. The algorithm is applied to a public dataset and an actual polyester fiber polymerization process. Experiment results show that the goodness of fit reaches 91. 6% and 98. 6% , which demonstrates the effectiveness and superiority of the proposed algorithm.