A product design scheme decision-making method combining electroencephalogram and eye movement with user similarity. The method comprises three parallel steps: (1) collecting original electroencephalogram (EEG) signals and removing artifacts and noises, extracting a β waveband of EEG about an emotion awakening degree and an α waveband of EEG about an emotional valence, and extracting potential changes of event-related potentials ERPs during decision maker scheme selection; (2) collecting an attention degree obtained on the basis of eye movement behavior data; and (3) obtaining a scheme selection time duration of the decision maker to obtain a scheme preference degree. The three data is combined to obtain implicit decision-making behavior data, and explicit decision-making data is obtained according to the selections of the decision makers; an association degree between the decision-making behavior data of different decision makers is calculated to obtain the user similarity. According to the method, explicit decision-making behaviors and implicit decision-making behaviors are collected, a correlation between the decision makers and schemes is established and a recommendation order is calculated, and an optimal scheme set based on a user similarity relationship is formed.