Abstract: Additive manufacturing demonstrates significant advantages in aerospace applications, including integrated fabrication of complex structures, support for lightweight design, and rapid iteration of engineering solutions. However, the expansion of technology application scale does not necessarily translate into improved commercialization performance. To establish a reusable evaluation approach for technology commercialization performance, this study takes aerospace additive manufacturing as the research context and selects three representative Chinese enterprises—Xi’an Bright Laser Technologies, Chaizhuo Aviation Technology, and Farsoon Technologies—as research samples. Based on publicly disclosed annual report data from 2019 to 2025, an evaluation index system covering four dimensions, namely market expansion, cost control, value realization, and technological investment, was constructed. The entropy weight method was employed to determine objective indicator weights, while the TOPSIS model was applied to calculate the comprehensive performance closeness of the samples. Furthermore, Grey Relational Analysis (GRA) was used to identify the key factors influencing performance variation. The results indicate that the aerospace businesses of the sampled enterprises generally exhibit a stage characteristic of “scale expansion accompanied by profitability pressure.” In the comprehensive evaluation, aerospace business gross profit (0.2277) and business revenue (0.2231) were identified as the two most heavily weighted indicators. In addition, R&D investment intensity (0.8873), total asset scale (0.7787), and aerospace business costs (0.7516) showed strong correlations with comprehensive performance changes, suggesting that technological investment, asset-intensive expansion, and cost rigidity are the core factors affecting the commercialization performance of additive manufacturing. The proposed analytical framework integrating “Entropy Weight-TOPSIS comprehensive evaluation and Grey Relational factor identification” is not only applicable to performance evaluation in aerospace additive manufacturing, but can also be extended to commercialization performance studies in other advanced manufacturing technologies