To solve the problem of complex structure for the discrete Hopfield neural network(DHNN), a structural optimization algorithm based on the contribution rate is proposed. The singular value decomposition method is used to design the connection weights. On the basis of the design, the contribution rate method is adopted to prune the connection weights. The structural complexity of the DHNN is reduced after structure optimization, and it makes the DHNN with sparse
network structure which is similar to biological neural network realize the structure optimization. Finally, the water quality evaluation and digital recognition are used to verify the effectiveness and feasibility of the structural optimization algorithm, and also demonstrate the effectiveness and applicability of the proposed algorithm for large scale DHNN.