Temporal logic has been extensively applied in domains such as formal verification and robotics control, yet it remains challenging for non-expert users to master. Therefore, the automated extraction of temporal logic formulas from natural language texts is crucial. However, existing efforts are hindered by issues such as sparse sample availability and the ambiguity of natural language semantics, which impede the accurate identification of implicit temporal semantics within natural language texts, thus leading to errors in the translation of the original natural language semantics into temporal logic formulas. To address this issue, a novel method for temporal logic semantic analysis based on a few-shot learning network, termed FSLNets-TLSA, is proposed. This method employs data preprocessing techniques to enhance the temporal semantic logic features of the text. The network architecture consists of an encoder, an induction module, and a relation module, which aim to capture the implicit temporal logic semantic information in the input text. In addition, an enhancement module is incorporated to improve the accuracy of monitoring semantic recognition. The effectiveness of the proposed method is validated through experimental evaluations conducted on three public datasets comprising a total of 3 533 samples, and a comparison with similar tools. The analysis demonstrates an average Accuracy, Recall, and F1-score of 96.55%, 96.29%, and 96.42%, respectively.