<p>The speed and accuracy of panoramic tourism image generation have constrained the development of panoramic virtual tourism videos, making them unable to be widely applied in the cultural and tourism industry. Therefore, this paper proposes a fast panoramic image generation method based on intelligent machine learning. In order to improve the application of 3D animation and virtual reality technology in the construction of digital tourist attractions, especially the 3D double-buffered animation simulation and visualization reproduction capabilities, this paper designs a 3D double-buffered animation simulation software production system using 3D visualization reconstruction and embedded VR architecture. The overall structure model of 3D double-cache animation simulation software is constructed, the embedded BS architecture system is used for the structural analysis of the system, and the network networking design of the animation simulation software production system is carried out by using IoT networking technology. Compared with the original HM algorithm, different QP values have a significant effect on the virtual reality prediction encoding time of CNN frames under the same convolution kernel size. In addition, under the same QP conditions, when the difference in BD bit rate and encoding time is not significant, the CNN algorithm has the best video encoding quality. Moreover, the use of CNN algorithm for selecting intra frame prediction encoding units in virtual reality video images can effectively shorten the time required for frequent rate distortion costs and obtain better intra frame encoding units, providing more valuable encoding modules for intra frame prediction encoding and improving the effectiveness of prediction encoding. Compared with commonly used image prediction encoding algorithms, this algorithm has better performance and can effectively improve the processing effect of panoramic virtual tourism images, providing tourists with a more reliable user experience.</p>