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To objectively evaluate the product design of tractor hoods, they have been set as variables, and the remaining components of the hood have been taken as the rations. Eye-tracking and semantic difference-based experiments were performed to determine the level of attention a user gave to the hood and an image evaluation value for the same; morphological analysis was used to deconstruct the structural elements of the tractor hood. The structural elements and image evaluation values were implemented as input and output layers, respectively, in a back-propagation neural network (BPNN) used to train and verify a user-evaluation prediction model for tractor hood designs. The results show that the BPNN model can accurately predict a user’s evaluation of the tractor hood design, thereby providing a reference for designers in terms of the tractor hood shape, and quantify user evaluations of the hood design.

User evaluation and eye tracking-based prediction model for tractor hood product design