In-situ leaching of uranium, as a green uranium mining technology, generates massive data in production operation, which are available for the big data analysis and trend prediction to improve the reliability of technicians in making production plans. In the current prediction algorithms, the attention mechanism in the temporal prediction model based on the encoder-decoder structure has the problems of computational complexity and high memory consumption. In this paper, we proposed a depthwise separable convolutional model, in which the semantic damage caused by fixed segmentation was reduced by the dynamic sequence segmentation module, and the depthwise separable convolutional mixer module was used to reduce the model running time and capture local features as well as global features. The results show that the Mean Square Error (MSE) and Mean Absolute Error (MAE) of the depthwise separable convolutional hybrid network model are reduced by 1.04% and 4.13% respectively, compared with Patch Time Series Transformer (PatchTST), and the proposed dynamic sequence segmentation module MSE and MAE are reduced by 7.32% and 5.03% respectively, compared to the original model; in the comparative performance analysis, the training speed of this model is 59.91% faster than the Trend Seasonal Decomposition Linear (Decomposition Linear, DLinear) model. The depthwise separable convolutional model can accurately predict the future trend of sulfuric acid injection volume in the production operation of the mining area, improve the existing prediction model for in-situ leaching data by solving the problem of long running time, large running memory, poor data fitting problems, which provide a theoretical and practical reference for the decision-making of in-situ leaching production.