In this study, a novel decision support model integrating spherical fuzzy sets enhanced with autoencoder-based dimensionality reduction, MEREC weighting, and CODAS ranking methods is proposed for high-dimensional, uncertain multi-criteria decision problems. The spherical fuzzy set structure allows decision makers to express their evaluations using three levels of membership (membership, non-membership, and hesitation). Thus, it produces linguistic evaluations appropriately to the nature of uncertainty.  In the numerical analysis, five elective courses, Python Programming, Java Programming, C# Console Programming, Visual Programming with C#, and Web-Based Programming, were evaluated based on 41 selection criteria.  The latent structures among these criteria were analyzed using the Autoencoder architecture, yielding 17 latent features with a reconstruction mean squared error of 0.016 as determined by an elbow-based reconstruction loss analysis, indicating negligible information loss beyond this dimension. The weights for these dimensions were objectively calculated using the MEREC method, which is based on the distinctiveness of each dimension in the decision process. The CODAS method was applied to rank the courses and provide decision support using the calculated weights. In the final stage, a comprehensive sensitivity analysis was performed to test the impact of changes in both dimension weights and decision-maker weights on the results Sensitivity analysis further confirmed the robustness of the proposed framework, with the top-ranked alternative preserved under ±10% criteria weight perturbations. The numerical results illustrate the practical applicability of the proposed framework and validate its effectiveness in handling complex evaluation structures.  Although the proposed framework is demonstrated through a programming course selection problem, the methodology is generic and can be readily applied to other complex decision-making scenarios involving high-dimensional, uncertain, and interrelated criteria.