提出了基于新阈值函数的深度残差收缩网络（DRSN-NTF），用于解决分布式光纤声传感（DAS）信号噪声强、识别难的问题。DRSN-NTF基于深度残差收缩网络（DRSN），使用新阈值函数代替软阈值函数，使其更能发挥信号噪声处理和分类识别能力。使用DAS系统采集周界入侵事件的实验数据，并通过添加高斯白噪声的形式，设计了6组不同信噪比（0 dB~5 dB）的实验。对比了4个模型的实验结果，由此考察DRSN-NTF的识别效果。结果发现：在强噪声的情况下，DRSN-NTF取得的平均测试准确率比DRSN高1.05%；随着信噪比的降低，DRSN-NTF的测试准确率高于DRSN的差值增大，表明DRSN-NTF在信号噪声处理和分类识别能力方面更强，能获得相对更高的识别准确率。因此，DRSN-NTF更加适用于DAS信号识别。

Distributed optical fiber acoustic sensing (DAS) signal has problems with strong noise and difficult recognition. To solve these problems, a deep residual shrinkage network based on new threshold function (DRSN-NTF) is proposed. DRSN-NTF uses new threshold function instead of soft threshold function on the basis of deep residual shrinkage network (DRSN), which makes it more capable in signal noise processing and classification recognition. DAS system is used to collect the experimental data of perimeter intrusion events, and six groups of experiment with different signal-to-noise ratios (0 dB?5 dB) are designed by adding Gaussian white noise. The experimental results of the four models are compared to investigate the recognition effect of DRSN-NTF. The results show that the average test accuracy of DRSN-NTF is 1.05% higher than that of DRSN in the case of strong noise. With the reduction of the signal-to-noise ratio, the difference between the test accuracy of DRSN-NTF and that of DRSN increases, indicating that DRSN-NTF is more capable in signal noise processing and classification recognition, which can lead to relatively higher recognition accuracy. Therefore, DRSN-NTF is more suitable for recognition of DAS signal.