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Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification. Signal-processing methods can extract the potent time-frequency-domain characteristics of signals; however, the performance of conventional characteristics-based classification needs to be improved. Widely used deep learning algorithms (e.g., convolutional neural networks (CNNs)) can conduct classification by extracting high-dimensional data features, with outstanding performance. Hence, combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance. A novel vibration recognition method based on signal processing and deep neural networks is proposed herein. First, environmental vibration signals are collected; then, signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms: the wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficient extraction method. Subsequently, CNNs, long short-term memory (LSTM) networks, and combined deep CNN-LSTM networks are trained for vibration recognition, according to the time-frequency-domain characteristics. Finally, the performance of the trained deep neural networks is evaluated and validated. The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.