This work proposes a pattern recognition method for the superposition state orbital angular momentum (OAM) of vortex beams based on convolutional neural network (CNN) and improved vision transformer (VIT). Organically integrating the local feature extraction advantages of CNN with the global fast classification ability of VIT driven by sparse attention mechanism, using three sets of Laguerre-Gaussian (LG) beam patterns with superimposed light field intensity distribution maps of ocean turbulence distortion as input, efficient and accurate recognition of end-to-end wavefront distortion is realized. MATLAB numerical simulation is adopted to simulate the superposition state LG beam in ocean turbulent environment, power spectrum inversion method is used to simulate ocean turbulence, and recognition accuracy and confusion matrix are used as evaluation indicators for OAM pattern recognition. The experimental results show that the CNN-VIT model exhibits excellent performance in OAM pattern recognition accuracy under different ocean turbulence intensities, wavelengths, transmission distances, and mode intervals. Compared with existing CNN and VIT, the proposed model improves recognition accuracy by 23.5% and 9.65% respectively under strong ocean turbulence conditions, thus exhibiting strong generalization ability under unknown ocean turbulence strengths. This demonstrates the potential application of the CNN-VIT model in OAM pattern recognition of vortex light superposition states.