Aiming at the problem of quantitative decoding performance of high-density parity-check codes, this study proposes a quantitative min-sum decoding approach based on deep learning, referenced as QMSND. In order to improve the decoding performance and efficiency, the proposed decoder can adjust the weight parameters of a Tanner graph dynamically, and quantize the input vector and the message of nodes at every iteration in nonuniform fashion via deep neural network. Computer simulation results show that the performance of proposed QMSND decoding with 8-bits presentation is very close to that of the float neural min-sum decoding without quantization. Therefore, the proposed decoding approach is easy to implement by hardware and has some practicability.