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Item classification is an issue among inventory managers who want to achieve key-point management of items with different emphases, thus prompting managers and researchers to pursue efficient and effective classification algorithms. In processing multiple-criteria inventory classifications, back-propagation neural network (BPNN) shows its superiority in balancing items’ multiple competing attributes. However, because the training processes and the final results of BPNN rely on the initial connection weights and thresholds, finding reasonable values of the two parameters is a challenge. This paper introduces biogeography-based optimization (BBO) and an adaptive mutation particle swarm optimizer (AMPSO) to BPNN to optimize the training parameters, i.e., the global initial connection weights and thresholds, of BPNN. On the basis of this, two hybrid classification algorithms—BBO-BPNN and AMPSO-BPNN—are presented. Real-life data from three cases are adopted to verify the effectiveness and feasibility of the two proposed hybrid algorithms. Experimental results demonstrate that BBO-BPNN and AMPSO-BPNN show higher classification accuracy than other hybrid models, i.e., BPNN, PSO-BPNN, DE-BPNN, and GA-BPNN, in the multiple-criteria inventory classification problem.