In order to further improve the performance of speech deception detection， a speech deception detection algorithm based on Denoising AutoEncoder （DAE） and Long Short-Term Memory （LSTM） network was proposed. Firstly， a parallel structure of DAE and LSTM was constructed， namely PDL （Parallel connection of DAE and LSTM）. Then， artificial features in the speech were extracted and put into the DAE to obtain more robust features. Simultaneously， the Mel spectrums extracted after adding windows to the speech and framing were input into LSTM frame-by-frame for frame-level depth feature learning. Finally， these two types of features were merged by the fully connected layer and the batch normalization， and the softmax classifier was used for the deception recognition. The experimental results on the CSC （Columbia-SRI-Colorado） corpus and the self-built corpus show that the recognition accuracy of the classification with fusion feature is 65.18% and 68.04% respectively， which is up to 5.56% and 7.22% higher than those of other algorithms， indicating that the proposed algorithm can effectively improve the accuracy of deception recognition.