For the structure health monitoring, this paper studies the structural damage detection and classification problems using the artificial immune system which has the extremely powerful capabilities of autonomy, initiative, adaptive and the bionic principle between learning and memory. An artificial immune pattern recognition and structural detection classification algorithm based on diagonal distance is proposed through imitating the immune recognition and learning mechanism. With the structure of benchmark proposed by the IASC-ASCE SHM working group as the platform, the damage detection and classification are tested. The simulation results show the classification rate based on the diagonal distance is better than Euclidean and Ma- halanobis. The relationship between the classification rate and the parameters which are clone rate and memory cell replacement threshold value is tested based on the diagonal distance, which show that the cloning rate should try to choose suitable parameter values in order to get a better classification success rate. The algorithm based on the immune learning and evolution can produce the high quality memory cells which effectively identify all kinds of structural damage model.