In order to enhance the reliability and efficiency of LDPC code decoding in MLC NAND flash memory, this paper proposes the MSWBF decoding algorithm. This algorithm no longer relies solely on the minimum LLR value as the basis for flip decisions; instead, it performs linear integration of the minimum LLR value associated with check nodes and variable nodes with their average LLR value, thus constructing a more stable local confidence estimation model. Furthermore, an exponential adjustment function is introduced in the calculation of flip indicators for variable nodes to dynamically strengthen the enhance the identification capability for low-confidence bits. Combined with the physical characteristics of flash memory, this paper establishes a channel model that includes RTN, DRN, CCI and creates generates soft information inputs through Monte Carlo simulation. Based on this, QC-LDPC codes are constructed and a decoding architecture is designed, comparing the performance of algorithms such as BF, WBF, MWBF, IMWBF and BP. Simulation results show that MSWBF improves the decoding success rate by 556% under strong interference conditions, and the average number of iterations decreases by over 30%, effectively improving decoding robustness and convergence efficiency. This algorithm is hardware-friendly and exhibits strong potential for practical implementation, providing a feasible improvement path for low-complexity soft decision decoding methods.