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Affected by near-surface absorption, seismic wave energy attenuation and phase distortion greatly reduce the resolution and signal-to-noise ratio (SNR) of seismic data, causing changes in seismic attributes much greater than other factors. Inverse Q filtering is a common method to compensate for these undesirable effects. To overcome the drawbacks of the traditional inverse Q filtering, such as the difficulty of parameter selection and the instability of wave amplitude compensation, we propose a new unsupervised inverse Q filtering method in a deep learning (DL) framework, using a forward attenuation operator based on the seismic wave attenuation theory to drive the network. The filtering strategy does not require actual training labels and avoids the numerical instability of the amplitude compensation. First, we design a hybrid convolutional neural network bidirectional LSTM (CNN-BiLSTM)-attention model for multivariate time series prediction and then take the data to be compensated as input for the DL network and the compensated data as output. The output is then attenuated using a forward attenuation operator constructed from the near-surface Q model. After that, the error between the attenuated data and the original input data is transmitted back to the DL network to modify the network output, and the error is minimized by optimizing the network parameters to generate the final compensation result. In the entire prediction process, there is no need to produce unattenuated data labels, which achieves the effect of unsupervised learning. The results with synthetic and field data demonstrate that the unsupervised method can effectively and stably compensate for seismic signals. Compared to the classical inverse Q filtering, the proposed method improves the resolution and SNR of seismic records.