Abstract: In the aerospace manufacturing industry, First Article Inspection （FAI） is a critical quality control process to ensure that products meet design requirements. During the FAI process, dimensional characteristics in engineering drawings must be identified and assigned balloon numbers to establish correspondence between dimensional features and inspection data. Traditional dimension ballooning methods mainly rely on manual identification and annotation, which are not only inefficient but also prone to omissions and numbering errors. To improve the efficiency of dimensional feature recognition, this paper conducts secondary development based on the CATIA platform and proposes an automatic recognition method for dimension balloons. By extracting properties of dimension objects in the CATIA drafting module and applying rule-based recognition, different types of dimensional features can be automatically identified. Balloon numbers are then generated automatically, and dimensional information is exported to Excel tables, enabling rapid association between dimensional data and inspection results. Practical application results show that the proposed method can significantly improve the efficiency of dimension ballooning and reduce human errors, providing an effective solution for the informatization and automation of quality inspection in aerospace parts manufacturing.