## 1 Introduction  
With the advent of the information age and the rapid advancements in 4 th generation mobile communication technology (4G) and 5 th generation mobile communication technology (5G), the network has become an indispensable tool and medium for accessing information in today's society [1]. At the same time, the constant updates and improvements in various intelligent devices and the significant increase in the number of mobile network users have posed significant challenges in terms of configuring network resources and constructing network infrastructure. With the ongoing development of 5G and the accelerated progress of 6 th generation mobile communication technology (6G), this remarkable growth trajectory will persist [2]. In recent years, the domain of network traffic has witnessed extensive research in areas such as network traffic classification, network traffic anomaly detection, and network traffic prediction [3; 4; 5]. These studies have provided invaluable insights for the advancement of the network field. However, in comparison to image recognition and natural language processing, the datasets published in the field of cellular network traffic prediction are relatively limited in diversity [6]. The quality of the datasets is low and they contain a significant amount of noise. To avoid compromising the experimental results with low-quality data, certain researchers have resorted to using private domain data. While this approach can enhance the model's efficiency, it hinders the ability to compare it to other models and advance the entire field. Furthermore, integrating multiple types of data through feature fusion can enhance the accuracy of predicting cellular network traffic [7]. Previous studies have successfully utilized considerable information, such as the number of base stations, weather data, and points of interest (POIs), to achieve remarkable results [8]. However, the traditional undirected graph overlooks the local similarities between samples and is more suitable for depicting global connection relationships. As the data similarity between adjacent regions in cellular network traffic is higher, the undirected graph fails to meet the demands of predicting such traffic. In terms of prediction methods, deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), have been extensively utilized in cellular network traffic prediction. Variants based on CNNs and LSTMs have also presented excellent effects. However, with the proliferation of mobile devices, the scale of network traffic is expanding. Some existing models fail to consider the spatial and geographical characteristics of traffic, resulting in issues of low accuracy and a lack of interpretability when handling long-term predictions. Consequently, there is an urgent need to design a long-term prediction model for cellular network traffic that incorporates multi-modal features. This paper addresses three predicaments: (1) The effective fusion of multi-modal features; (2) enhancing the accuracy of predictions; and (3) verifies the effectiveness and feasibility of the proposed method in the real world. With the intention of resolving these issues, this paper makes the following contributions:  
* An attributed KNN graph is constructed. According to the similarity of nodes, the most similar \(K\) nodes are extracted and input into the model, which avoids the low prediction accuracy caused by the uneven spatial and temporal distribution of cellular traffic. In addition, the attributed KNN graph also combines multi-modal features, such as POIs, to provide more information for the model. * A prediction model called DBGTNN-Att is developed. The model utilizes spatial graph convolution (SGC) to process the multi-modal features present in the KNN graph and subsequently feeds the processed features into dual branch-gated convolution with an attention mechanism (DBGCA) for time-series prediction to capture the complex relationships in the data.