The work aims to investigate the relationship between tractor posture and image preference, and then guide the design of tractor styling. Multivariate scaling analysis and cluster analysis were adopted to screen posture samples of tractors. The morphological analysis was employed to decompose and calibrate the posture features of typical samples, and the image space of tractor posture was constructed through lexical clustering experiments. By combining the semantic differential method and questionnaire survey, user image preferences for the typical tractor samples were obtained, and the correlation between tractor posture features and image was determined through grey relational analysis. Eight typical tractor posture samples were obtained and five representative semantic word pairs were extracted under four semantic dimensions of tractor posture. The characteristic framework and semantic structure of tractor posture were clarified, and the image cognition rules of tractor posture were inductively derived. Tractor posture features embody image information, and the research results can provide designers with references for interpreting user image cognition.