Aiming at the issues of insufficient accuracy in detecting complex temporal events and the neglect of inter-event correlations of deep event detection models， a deep temporal event detection algorithm based on temporal logic， DSTL （Deep Signal Temporal Logic）， was proposed. In the algorithm， for one thing， a framework of signal temporal logic was introduced， and events in time series were modeled using Signal Temporal Logic （STL） formulae to consider the logicality and temporality of events in time series comprehensively. For another， a neural network-based base classifier was utilized to detect the occurrence of atomic events， and detection of complex events was aided by structures and semantics of STL formulae. Additionally， neural network modules were employed to replace the corresponding logical conjunctions and temporal logic operators to provide neural network modules supporting GPU acceleration and gradient descent. Through experiments on six time series datasets， the effectiveness of the proposed algorithm in temporal event detection was validated， and the model using DSTL algorithm was compared with deep temporal event detection models using MLP （Multilayer Perceptron）， Long Short-Term Memory （LSTM） network and Transformer without using this algorithm. The results indicate that the model using DSTL algorithm has an approximate 12% improvement in average F1 score on five event categories， with an approximate 14% improvement in average F1 score for three categories of cross-time point events， and it has better interpretability.