## 5 Conclusions  
This article takes Yingkou port's sea-rail intermodal container throughput prediction as an example, selects 31 indicators related to sea-rail intermodal container throughput, supplements the collected monthly data using the multiple interpolation method, and then conducts Pearson and Spearman correlation analyses. Based on the correlation indicators, k-means grouping is used, and a WA is performed according to the total weight proportion. Next, the quadratic interpolation method is used to upgrade the monthly data into daily data, which horizontally improves the data quality. The WACNN model is used to solve this prediction problem. Simultaneously, three time series prediction models and two ML models are used for prediction, comparison, and validation, proving that the WACNN model has the best overall prediction performance. The results show that the weight assignment can effectively assist a CNN with capturing and learning local spatial features through feature enhancement. And the WACNN has a strong ability to deal with the regression prediction problem and has strong robustness and strong interpretability.  
Traditional time series models typically rely on lagged features as inputs to capture temporal dependencies. However, such methods may result in a systematic bias of the predicted results relative to the true values, due to the time interval between the input features and the target prediction. This model avoids cumulative errors caused by multi-step lag by strictly aligning the time steps of the input and target. The experimental results demonstrate that this prediction method achieves high prediction accuracy. Using the WACNN model to predict the container throughput of the port will help enterprises plan the use of transportation resources and paths to cope with various uncertainties. After predicting the volume of container throughput, the relevant departments will also be able to make appropriate emergency plans to prevent a shortage of capacity caused by a surge in freight volume. In addition, although the prediction results of the model have a certain degree of reference, the accuracy needs further improvement. In order to improve the prediction accuracy, the WACNN model can be combined with other deep learning models so that the accuracy of the prediction results can be improved to a certain extent.  
Y.W.: Writing--original draft preparation, Conceptualization, Data curation, Methodology, Writing--reviewing and editing. W.L.: Supervision, Methodology, Validation, Software, Writing--reviewing and editing. X.Q.: Supervision, Writing--reviewing and editing. Y.Y.: Software and Writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hubei Provincial Natural Science Foundation Project-Xiangyang innovation and development joint fund (2025 AFD064) and the Philosophy and Social Science Research Project of the Hubei Provincial Department of Education (No. 23Q171).  
The data that support the findings of this study are available from the authors on reasonable request.  
The authors acknowledge the Open Fund of the Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle. Finally, the authors are grateful for the useful contributions made by their project partners.  
Author [PERSON] was employed by the company Xiaobe Lingang Development Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.