A method for grading subjective questions includes sentence preprocessing, feature extraction, feature fusion, similarity calculation and comprehensive scoring. Wherein, the sentence preprocessing isused for clause, word segmentation, keyword detection, part-of-speech tagging and sentence emotion analysis of the target paragraph. The feature extraction algorithm is used for extracting a word vector, a sentence vector, a word structure and a syntactic structure. The feature fusion is used for fusing a target paragraph containing M sentences into a contrast template containing N templates (N (M): the similarity calculation is used for calculating word similarity and sentence similarity. The comprehensive score is used for constructing a weight model according to the word similarity, sentence similarity, word structure similarity, syntactic structure similarity, keyword score and affective score between the student answers and the comparative template, and then grading the student answers. The invention adapts to the scoring requirements of subjective questions of various disciplines, and good scoring effect can be obtained through training of a small number of samples.