The application of artificial intelligence and machine learning technology in the field of wireless communication has received great attention. The success of artificial intelligence in speech understanding, image recognition, natural language processing and other fields shows its great potential to solve the problem of difficult modeling. Aiming at the problem of 3D reconstruction of defocused blur spots, a method based on adaptive defocus blur radius estimation and double hidden layer BP neural network is proposed. This method first uses an adaptive segmentation algorithm to extract the approximate elliptical spot in the defocused blurred image, and then uses the gradient amplitude distribution to extract the defocused blurred area, thereby calculating the blur radius. Then, using the network to adaptively learn the geometric structure relationship between the target 3D position, the target image point position and the defocus blur radius, it is established that the center pixel coordinates of the light spot and the corresponding defocus blur radius are used as input, and the 3D coordinates of the target are used as input. Output, the two hidden layers in the middle have established a neural network for the 3D reconstruction of the defocused blur spot. The experimental results show that the double hidden layer BP network model proposed in this paper can realize the 3D reconstruction of the blurred spot after training, and the method proposed in this paper has higher 3D reconstruction accuracy than without considering the defocus blur effect.