In order to study the impact of surface roughness on the cyclic shear characteristics of the Soil-Rock Mixture and concrete interface, a series of cyclic shear tests were conducted using a large indoor direct shear apparatus. The effects of three concrete surface roughness coefficients JRC (0.4, 9.5, 16.7), five rock content levels (0%, 25%, 50%, 75%, 100%), and three cyclic shear displacement amplitudes (1, 3, 6 mm) on interface cyclic shear stress and Soil-Rock Mixture shear deformation were analyzed. A Bidirectional Long Short-Term Memory (BoBiLSTM) model was proposed, utilizing Bayesian optimization and k-fold cross-validation for hyperparameter tuning to streamline the model parameter selection process and enhance the prediction accuracy of the stress-strain relationship under cyclic loading. The experimental results show that, under five rock content levels, as the concrete surface roughness coefficient and cyclic shear displacement amplitude increase, the interface average peak shear stress increases accordingly. The interface average peak shear stress of the sample with 75% rock content is the highest; in terms of vertical displacement, the sample with 50% rock content has the maximum displacement, while the sample with 25% rock content has the minimum. The two types of samples show different soil deformation patterns in the two shear directions during the cyclic shearing process; as the shear displacement amplitude increases from 1 mm to 3 mm and 6 mm, the greater the concrete surface roughness, the smaller the change in shear stiffness and damping ratio. Compared to traditional Long Short-Term Memory (LSTM) models, the BoBiLSTM model demonstrated improvements in the average metrics of R2, RMSE, and MAPE by 0.32%, 57.25%, and 72.32%, respectively.