Aiming at the poor expressive ability of image statistical information during the reconstruction process of traditional 3D image reconstruction method based on virtual reality technology, resulting in low accuracy of 3D image after reconstruction, a new image detection and 3D image reconstruction based on virtual reality technology are studied method. This paper first proposed a new twolevel cascade convolutional neural network structure. The first level of the network predicts target positioning based on the image-level labels of the training image, generates a bounding box of the target in the original image, and generates a cropped image. The cropped image is input to the second-level network. The cropped image may contain areas where the target is stuck in the original image. Level 2 networks only use the adhesion area as training data.Secondly, the visualization software development platform and virtual reality 3D image processing software are selected as the platform for 3D image reconstruction. After the original image is imported into the computer through data input and file analysis steps, the original image is detected. The detected image is in the virtual in the real software, the bounding box method is first used to construct the three-dimensional data field of image reconstruction, and the three-dimensional direct volume of the image is drawn according to the three-dimensional data field of image reconstruction. Preferably, the three-dimensional image reconstruction output formula is obtained through the threedimensional image direct volume to realize the three-dimensional image reconstruction based on the virtual reality technology. The simulation results show that the method proposed in this paper can effectively detect images. The average traversal coverage of 3D image reconstruction is up to 0.979, and the reconstruction accuracy is higher than 0.97. INDEX TERMS image classification; distributed network representation learning; deep learning; neighbor reconstruction.