Addressing the issue of data anomalies in radar countermeasure reconnaissance signals caused by susceptibility to interference in complex electromagnetic environments， this paper proposes a multidimensional temporal intelligent fusion network （MDTFusionNet）. The architecture integrates temporal convolutional network（TCN）， long short-term memory（LSTM）， and self-attention mechanisms with a gated network that dynamically adjusts module weights through a gated network， and optimizes model robustness by combining weight sparsity constraints. This constructs an anomaly detection model capable of not only capturing short-term fluctuations in radar signals and grasping long-term trends but also dynamically focusing on critical pulse information. To verify its effectiveness， multi-layer perceptron（MLP）， TCN， and LeNet networks are used for comparison， and evaluations are conducted from the perspectives of loss function and accuracy. Experimental results show that the loss function outliers of MDTFusionNet are significantly smaller than those of traditional models， and the accuracy is higher. Ablation studies further confirm that each component in MDTFusionNet has its respective role， enabling better learning of temporal data feature distributions and accurate anomaly detection， verifying its superiority and practicality in radar countermeasure reconnaissance signal anomaly detection.