A bidirectional gated recurrent temporal network model was proposed, and a CGCE (classification guided cross-entropy) loss function was incorporated to mitigate misclassification of high-risk intentions. The bidirectional gated recurrent temporal network model was composed of a BiTCN (bidirectional temporal convolutional network), a BiGRU (bidirectional gated recurrent unit), and an attention module. The BiTCN was used to capture global features, the BiGRU enhanced understanding of complex data through bidirectional learning, and the attention module dynamically assigns feature weights to emphasize key information. The introduction of CGCE enhanced the model's sensitivity to high-risk misjudgments, thereby reducing the misjudgment of high-threat intentions. The proposed model achieves an accuracy of 98.58% and outperforms the comparison methods across accuracy, precision, and F1-score. Furthermore, the incorporation of CGCE further improves the model’s accuracy and significantly reduces misclassifications of high-threat intentions, validating the effectiveness of the proposed model and CGCE in aerial and maritime target intention recognition.