The existing detection approaches can not detect unknown recommendation attacks effectively. Aiming at this problem, an approach based on bionic pattern recognition is proposed. Firstly, items are partitioned into different windows according to their popularity. The ratings given by users for the items in the windows are regarded as the occurrences of random events. Further, information entropy is used to extract features of rating distribution as genuine features for the detection of recommendation attacks. In addition, the technique of bionic pattern recognition is used to cover the samples of genuine profiles in the feature space. Test data outside the coverage are judged as recommendation attacks. The experimental results on the MovieLens dataset show that the proposed approach has high hit ratio and low false alarm ratio when detecting unknown recommendation attacks.