Dynamic emotion recognition and learning motivation prediction in Chinese second language acquisition under cultural...

To address the core issues of low accuracy, poor cultural adaptation, and insufficient efficiency in learning motivation prediction in cross-cultural Chinese second language acquisition scenarios, this paper proposes the ED-CM-MP model, which integrates dynamic sentiment recognition, cultural adjustment modeling, and lightweight temporal prediction. This model uses DistilBERT+Gated TCN to construct a dynamic sentiment module to extract temporal sentiment features, GraphSAGE to adjust for cross-cultural differences, and Temporal Fusion Transformer to achieve efficient motivation prediction. Experiments on the HSK and VIDAS cross-cultural datasets show that the model achieves the best core prediction performance: MAE of 0.28-0.29 and F1 Score of 0.91-0.92, representing a 10.2% improvement in accuracy compared to the best baseline model; inference latency as low as 38.5-39.2 ms and FLOPs of only 12.6-13.1 G, representing a 20.3% improvement in efficiency compared to MobileNetV3; and a cultural adaptation score of 0.94-0.95, representing a 21.8% improvement in cross-cultural generalization ability compared to U-Cast. Ablation experiments validated the necessity of the three modules working together; removing any module resulted in a performance decrease of 3.6%-7.2%. Stability tests showed that the model exhibited excellent robustness with performance fluctuations of ≤ 5.4% in a small sample scenario with 10% labeled noise and 2000 training samples. This research demonstrates that the ED-CM-MP model achieves a triple breakthrough in motivation prediction-accuracy, efficiency, and generalization-providing an efficient and feasible technical solution for intelligent teaching intervention in cross-cultural Chinese second language acquisition.