The single-mode of ridge waveguides is important to integrated optoelectronic components and on-chip lasers. The traditional finite-difference time-domain method consumes a lot of computing resources to design the mode. A deep learning framework was proposed for rapid identification of single-mode regimes in silicon-on-insulator (SOI) ridge waveguides. A Bayesian-optimized artificial neural network was applied to modal classification. By designing a ridge waveguide mode classification method that combines an optimized artificial neural network with Bayesian algorithms, high-precision rapid classification of ridge waveguide modes was achieved. By learning the mapping relationship between the geometric structure of the ridge waveguide and the single-mode characteristics, the network can predict the single-mode properties of unknown ridge waveguide geometric parameters. The results show that 99% classification accuracy is achieved by the model with only 252 training data. The classification time of a single data point is reduced from 52 seconds of the traditional time-domain finite difference algorithm to 5 milliseconds, and the computational efficiency is improved by about 1 000 times. At the same time, the model has good generalization ability. This algorithm is expected to shorten the design cycle of optical waveguides, which has a certain supporting role in the design and preparation of large-scale photonic chips.