Abstract: The Kuroshio front in the East China Sea is the boundary between the Kuroshio Current and the East China Sea shelf water, exerting significant influence on the marine environment and climate variability in the region. In this study, based on JCOPE2M reanalysis data and WOA23 climatological average data, the Kuroshio front is identified using isotherms and the meridional maximum temperature gradient. The seasonal and interannual variations of the Kuroshio front strength are analyzed statistically. Considering the multi-scale characteristics of the Kuroshio front, a prediction model for the Kuroshio front in the East China Sea is constructed with the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) networks. The results indicate that the proposed method for front detection and optimization is suitable for both the entire water column and weak front regions, achieving satisfactory results in identifying the Kuroshio front in the East China Sea. The strength sequence of the Kuroshio front extracted through frontal analysis in the East China Sea can be decomposed into trend, periodic, and irregular fluctuation components. The sub-surface front exhibits significantly higher strength compared to the surface and mid-layer fronts, with an increasing trend of 0.033 4 (℃/km)/a. The VMD-LSTM combined model shows excellent predicting performance, with higher accuracy and better agreement with the original values of the Kuroshio front strength. The prediction accuracy is significantly improved compared to the seasonal autoregressive integrated moving average model and the standalone LSTM model, indicating its potential for broader applications.