An information encoding and decoding scheme based on vector vortex beam and deep learning was proposed to improve the encoding novelty and mode recognition accuracy in vector optical communication. A novel encoding method was designed to embed grayscale image information into the characteristic parameters of vector vortex beams. Equivalent phase screens were used in the laboratory to simulate the effects of no, weak, moderate, and strong atmospheric turbulence environments on beam wavefront. A mode-recognition neural network consisting of cascaded dense blocks was constructed to decode the polarization distribution characteristics of beam modes at the receiving end. The mode recognition accuracies of the no, weak, moderate, and strong atmospheric turbulence environments are 100%, 99.96%, 99.93%, and 93.75%, respectively. A Lena grayscale image with a resolution of 80×80 was experimentally encoded and transmitted in each of the four atmospheric turbulence environments to verify the feasibility of the scheme. The bit-error-ratios of the image decoding are 0, 1.56×10^-4, 7.8125×10^-4,^{and 4.898×10-2, respectively. The results demonstrate that the proposed scheme can achieve high-quality encrypted encoding, transmission, and decoding of grayscale image information, and it has potential application value in the structured optical communications in atmospheric turbulence environments.}