Aiming at the problem of spectral data aliasing in multi-band laser interference images

this paper proposes an autocorrelation extraction classification algorithm based on associated feature information. First

the auto-correlation calculation mode is used to complete the data attribute classification. Then

an autoregressive average model based on feature data mining is designed by using attribute membership function as a criterion. Finally

the data mining is completed by cyclic iteration of time-decay weight coefficients. In the experiment

a mixed laser light source is tested. The algorithm is compared with traditional mean classification algorithm and least square method classification algorithm. Experimental results show that the classification error of the algorithm is 0.110

which is better than the other two algorithms（0.191 and 0.167）. And under the condition of the same total amount of data

the proposed algorithm takes the least time. It can be seen that this algorithm has a very good effect on the effective separation of aliasing data in multi-band laser interference images. It has certain practical value in the application of aliasing data classification.