This paper proposes an algorithm for multilayer feature extraction and adaptive fusion based on deep convolutional neural network (DCNN) to solve the problem of image type recognition of the lowlevel wind shear signal scanned by the laser radar. The method effectively makes up for the information loss in the process of layerbylayer network training. First of all, the characteristics of the network layers of lowlevel wind shear signal image are extracted by using DCNN modeling and the characteristics are L2 norm standardized. Then the L2 norm standardized characteristics are put into singlelayer CNN for adaptive fusion in the form of multichannel image and the fusion characeristics are sent into the support vector machine for classification recognition. The results show that the average recognition rate of image type recognition of the low level wind shear signal obtained by using the proposed algorithm is 98.1%, being improved compared with the other four algorithms. The algorithm can effectively realize image type recognition of the low level wind shear signal.