  In this paper, we consider an reconfigurable intelligent surface (RIS)-aided
frequency division duplex (FDD) massive multiple-input multiple-output (MIMO)
downlink system.In the FDD systems, the downlink channel state information
(CSI) should be sent to the base station through the feedback link. However,
the overhead of CSI feedback occupies substantial uplink bandwidth resources in
RIS-aided communication systems. In this work, we propose a deep learning
(DL)-based scheme to reduce the overhead of CSI feedback by compressing the
cascaded CSI. In the practical RIS-aided communication systems, the cascaded
channel at the adjacent slots inevitably has time correlation. We use long
short-term memory to learn time correlation, which can help the neural network
to improve the recovery quality of the compressed CSI. Moreover, the attention
mechanism is introduced to further improve the CSI recovery quality. Simulation
results demonstrate that our proposed DLbased scheme can significantly
outperform other DL-based methods in terms of the CSI recovery quality
