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Real-time traffic speed prediction is one of the most essential parts of the Intelligent Transportation System. In recent years, with the development of artificial intelligence technology, such as in-depth learning, new prediction methods have emerged in endlessly and achieved good results. However, the spatio-temporal information, traffic environment, and their interaction have been hardly depicted in these methods. Therefore, a novel traffic speed prediction method based on temporal clustering analysis and deformable convolution neural network (TCA-DCNN) is proposed in this paper. Temporal clustering analysis (TCA) based on Differential Evolution and hierarchical clustering can adaptively distinguish traffic environment by discriminating the traffic speed variation pattern. By further introducing the deformable convolutional kernels, the characteristics of spatio-temporal traffic speed variation are precisely located in deformable perception fields. After that, a set of DCNN models are trained by using the data processed by TCA, and the output of one of the basic DCNN model is selected as the final traffic speed prediction result. The simulation results based on the measured data in Hangzhou, China, show that the TCA-DCNN algorithm has better performance than other algorithms in traffic speed prediction.