## 1 Introduction
### Contribution and Paper Organization  
Regression prediction using deep learning models can effectively improve prediction accuracy, but there are still some problems. The first is the data level, with a variety of data metrics, and unsuitable data can lead to lower prediction accuracy. Secondly, there is the feature engineering aspect. Considering that deep learning models require more data, we need to choose a suitable method for data enhancement. Finally, the problem of container throughput prediction using DL is faced with a complex network construction and parameter selection, which requires the construction of a suitable structure for the specific problem.  
These problems make the container throughput prediction problem difficult to solve. In order to solve these problems and improve the accuracy of container throughput prediction, first of all, for the selection of the data indicators, we summarize the selected indicators in existing container throughput prediction problems and obtain a set of division standards by generalization. Next, we group the indicators and assign weights, and at the same time increase the frequency of the data to achieve the goal of improving the quality of the data. And then, in order to select the appropriate network structure as well as the parameter combinations, we construct multiple sets of network structures as well as parameter combinations. Finally, in order to validate the WACNN model's effect, multiple mainstream DL and ML models are used for validation and comparison. The main contributions of this study are summarized as follows:  
1. Firstly, a preliminary screening of the indicators based on qualitative analysis methods, completion of the selection of indicators by formulating scientific division standards, and using missing value analysis to exclude the variables with too high a missing rate; then, using multiple interpolation to fill in the missing data.
2. For the feature engineering, we perform a Pearson as well as a Spearman correlation analysis on the selected indicators. Based on the correlation results obtained, the metrics are categorized into multiple groups using a k-means clustering analysis and assigned appropriate weight sizes based on their combined weights. At the same time, the dataset size is increased horizontally using quadratic interpolation.  
In order to select the appropriate model structure, we propose a variety of different network structures and select the most appropriate network structure as well as parameter selection through a comparative analysis. At the same time, in order to verify the prediction effect of the WACNN model, this paper uses a variety of mainstream DL as well as ML models to verify that the WACNN model has the best prediction performance.  
The rest of the paper is organized as follows: Section 2 briefly reviews the relevant literature on indicator selection, feature engineering, and DL prediction. Section 3 is an introduction to the methodology, including the indicator selection, feature engineering, WACNN model construction, and evaluation index system selection. The data processing; constructing the complete network structure; and finally, conducting the experiments are shown in Section 4. Finally, in Section 5, the research results are summarized.