## 1 Introduction  
In recent years, due to the rapid development of the Chinese economy, demand for the transportation of goods has also rapidly increased, bringing great challenges to China's transportation industry. In this situation, heavy haul railways have become a main development direction for freight railways [1]. Heavy haul trains have bigger load-carrying capacities and longer lengths, making them more difficult to operate. The existing wireless communication system, based on the global system of mobile communications for railways (GSM-R), has the disadvantages of insufficient bandwidth, poor anti-interference ability, and insufficient security [2]. Given this, Shuo Huang Railway applies the long-term evolution for railways (LTE-R) network to carry core wireless communication services, such as the synchronous control and wireless reconnection of heavy haul trains. The LTE-R wireless communication system carries core communication services for heavy haul trains. Therefore, the communication performance of LTE-R cells directly affects the operational safety of heavy haul trains. At present, Shuo Huang Railway mainly collects communication performance indicator data for each cell using drive tests and then manually screens for cells with degraded communication performance by observing the deterioration of key performance indicators. However, due to the large amount of drive test data, this traditional manual analysis detection method is inefficient and accuracy largely depends on the personal abilities and work status of the operational and maintenance personnel [3]. In fact, it is difficult to accurately determine whether a certain cell has experienced communication performance degradation solely based on road test data. The evolved radio access bearer (E-RAB) abnormal release rate (EARR) of LTE-R core communication services is a more practical indicator for evaluating the communication performance of LTE-R cells [2]. But EARR is a statistical indicator that is difficult to obtain in real time. Therefore, it is necessary to develop more efficient and intelligent methods for the detection of LTE-R cells with degraded communication performance by establishing the accurate mapping relationship between the drive test data and EARR. Some experts and scholars have already studied detection methods for abnormal wireless communication performance. These detection methods are mainly divided into two types: shallow model methods [4; 5; 6; 7] and deep model methods [8; 9; 10; 11; 12]. Shallow model methods utilize traditional shallow model machine learning methods to detect abnormal wireless communication performance. However, shallow model methods have shortcomings in extracting complex nonlinear patterns from data. With the continuous development of deep learning technology, more researchers are attempting to use deep model methods to detect abnormal wireless communication performance. Since the mapping relationship between drive test data and EARR is a complex nonlinear mapping relationship, and deep model methods have advantages in dealing with the problem of complex nonlinear mapping [13], this paper chooses to use deep model methods to establish this mapping relationship. Furthermore, due to the high reliability of the LTE-R communication system, records for cells with degraded communication performance are very rare (the ratio of normal data to abnormal data is close to 30:1), so the main difficulty in detecting LTE-R cells with degraded communication performance is how to build an imbalanced data classification model with high classification accuracy. In order to solve the problem of detecting LTE-R cells with degraded communication performance, we fully consider the characteristic of communication performance indicator data being sequentially distributed along railways, and we treat the detection problem as an imbalanced sequence data classification problem. Then, based on the basic principle of shapelet transformation, we realize the feature extraction of sequence data by transforming CNN.