In this paper, I am going to propose a new Boolean Structured Variational Autoencoder Deep Learning Network (BSvarautonet) built on top of BSautonet, based on the concept of monotone multi-layer Boolean algebra. Kullback—Leibler (KL) divergence used in traditional Variation Autoencoder has convergence problem and numerical instabilities. Due to the Boolean Structured design of BSautonet, the bottleneck latent space embeddings is naturally distributed in multi-variables Gaussian distribution. By applying a whitening normalization on the latent space, it will transform the latent space to unit Gaussian distribution. Through analysis of the datapoints in latent space and generated MNIST digit images, it has shown that it has all the properties of variational autoencoder. The BS autoencoder is a masked noise denoising model, therefore it can acts like a diffusion model to incrementally generate a digit image from a noisy one through repeated applications of the autoencoder model.