In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.