There are two application problems in the iris multi-category recognition scenes, namely: when adding a new template category, the expansion convenience problem caused by the difficulty of category expansion, and the environment independence demand caused by the distortion of the iris image information. To solve these two application problems, we propose an iris recognition model. The model is divided into two stages, namely: the first recognition stage and the second recognition stage. According to the orderly arrangement in the same category sample clustering range of each dimensional feature value, a 32-dimensional continuous vector space is formed as the first recognition stage feature knowledge. The 32-dimensional ordered continuous array on the basis of grayscale stable features is used as the feature knowledge in the second recognition stage. The result in the first recognition stage is divided into three types: result category, pending category, and elimination category. The second recognition stage is a specific process that is initiated when the result category is not unique or there is a pending category. Through a specially designed non-template matching function, accurate result can be obtained in the pending categories. The results of experiments with different iris libraries verify that continuous feature space based on image texture can effectively reduce the influence of image information distortion. Additionally, each feature data dimension as a training unit is conducive to the independent training of feature knowledge in single category. It can add new iris categories without interference and solve the problem of expansion convenience.