In order to cope with the increasing overhead of channel state information （CSI） feedback in massive multiple-input multiple-output （MIMO） systems， deep learning-based CSI feedback networks （such as Transformer） have received extensive attention and become very promising intelligent transmission technologies. To this end， this paper proposes a simplification method of CSI feedback Transformer network based on data clustering， which uses clustering-based approximate matrix multiplication （AMM） to reduce the computational complexity of the Transformer network in the feedback process. In this paper， we focus on the computation of the fully connected layer in the Transformer network （equivalent to matrix multiplication）， adopt the simplification methods such as product quantization （PQ） and MADDNESS， analyze their influence on the computational complexity and system performance， and optimize the algorithm according to the characteristics of neural network data. Simulation results show that the performance of the CSI feedback network based on the MADDNESS method is close to that of the exact matrix multiplication method with an appropriate parameter adjustment， and the computational complexity can be greatly reduced.