To implement optimization for mechanical concepts acquired by function analysis more effectively, BP neural network is adopted to structure multilevel evaluation model, which capitalizes on the features of nonlinearity, self-organization, and fault tolerance of neural network. By using appropriate data sets to train the neural network, expertise is acquired and expressed using a trained weight and threshold matrix. Once evaluation objectives of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model, the optimal concept can be obtained. During the process, neural network is used to solve the bottle-neck problem of knowledge acquisition and expression and can be viewed as knowledge base and reasoning engine for the optimization. Hence the proposed evaluation model can effectively deal with concept evaluation and optimization problem with multilevel objective system.