Multimodal Data Fusion for Enhanced CNN-LSTM Based Intelligent Football Training and Tactical Analysis

Abstract

Existing football training and tactical analysis systems often suffer from inaccurate feedback and biased tactical judgments due to reliance on single-modality data and fragmented information. To address these limitations, this study proposes a deep multimodal fusion framework that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Specifically, CNN is employed to extract spatial features from video frames and sensor signals, while LSTM captures temporal dynamics of sequential data. To ensure consistency across heterogeneous data sources, feature normalization and time alignment strategies are applied. An attention mechanism is further introduced to adaptively allocate weights to different modalities, thereby enhancing the representation of critical features. In addition, a multitask learning scheme with dual loss functions—training quality evaluation and tactical behavior classification—guides the model to optimize both action recognition and tactical inference simultaneously. The entire system is constructed in an end-to-end manner, from multimodal data input to training feedback and tactical analysis output. Experimental evaluation demonstrates that the system achieves 91.3% accuracy in action recognition, 89.7% accuracy in tactical classification, and maintains a training feedback error within 2.4%. These results highlight the system’s potential for refined training management and efficient tactical analysis, offering a viable pathway toward intelligent football systems.