The identification of chip surfaces primarily includes the manufacturer’s icon, serial number, and other text, which serve as unique identifiers for the chip. Automatic identification technology plays a crucial role in the chip packaging and testing process. Given the presence of noise, various character forms, incomplete characters, and varying text lengths in industrial environments, this study focuses on achieving high-precision identification of complex text on chip surfaces. To address this, a recognition method based on feature extraction and reconstruction of the identification text is proposed. This method extracts recognizable features from damaged identification text and enhances the classification feature of chip text, thereby improving recognition accuracy. The novelty of this study lies in extracting and enhancing the classification features of chip identification text from the perspective of recognizability features, which enhances the recognition capability of complex text on chip surfaces.