1. An image object category recognition method, comprising the following steps: an image feature extracting step (S 1 ) of extracting feature points of all sampling images in N known categories with a feature point extracting method, and establishing a known category—sampling image—feature point correspondence, where N is a natural number greater than 1, and each category comprises at least one sampling image; a cluster analyzing step (S 2 ) of performing cluster analysis on all of the feature points extracted using a clustering algorithm, and dividing the feature points into N subsets; an object category determining step (S 3 ) of determining an object category C n for each of the subsets; a common feature acquiring step (S 4 ) of acquiring common features among the images in each object category C n with a search algorithm, where C n is the n th object category, and n is a positive integer no more than N, wherein the step S 4 comprises at least the following sub-steps: S 401 of searching a set of common feature points sharing common features among images included in each object category C n by means of the search algorithm; and S 402 of additionally mapping sampling images having the largest number of common feature points among the set of common feature points from the each object category C n based upon the set of common feature points searched by means of the known category—sampling image—feature point correspondence, and using the sampling images as average images of the object category C n ; and after the step S 4 , an on-line image recognizing and categorizing step S 5 of recognizing and categorizing an image to be categorized by comparing the image to be categorized with at least one of the common feature points among the images in each object category C n and the key feature points in the average images in each object category C n .