This study develops a CRITIC-weighted Grey Closeness Relational Degree-based TOPSIS (GRCD∙TOPSIS) method for evaluating input-output performance under small-sample conditions with a limited number of decision-making units (DMUs). To enhance comparability, the original input and output data are adjusted for variations in system scale and transformed logarithmically. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is applied to determine objective weights for input and output indicators separately. Within the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) framework, positive and negative ideal solutions are established based on the cost-type nature of inputs and the benefit-type nature of outputs. Grey closeness relational degrees to the ideal solutions are computed from CRITIC-weighted absolute distances and subsequently synthesized to yield input and output scores, with overall efficiency measured as the ratio of the output score to the input score. An empirical study of higher education systems across 31 provincial-level regions in mainland China demonstrates that the proposed method effectively differentiates provincial efficiency under limited-DMU conditions and reveals significant regional heterogeneity in input, output, and conversion efficiency. At the regional level, the Northeastern region exhibits the highest efficiency, followed by the Western, Eastern, and Central regions. The approach offers a transparent and data-adaptive tool for small-sample input-output performance evaluation, with promising applicability in contexts where conventional DEA-like methods are infeasible due to sample size constraints.