In response to the automated detection of weld seams and high-precision image recognition requirements for small-diameter clean pipes before leaving the factory, an intelligent weld seam recognition method based on gray-level co-occurrence matrix feature extraction was proposed and integrated into a small pipe weld seam detection robot system. This method accurately distinguished between weld seams and pipe walls by extracting four typical texture features: entropy, contrast, angular second moment, and inverse differential moment from the sampled images, combined with threshold judgment. Upon detecting the weld seam, the system controlled the camera module to rotate and capture images of the weld seam area and restored the complete weld seam image through image stitching. To verify the performance of the algorithm, detection experiments were conducted on different types of pipeline welds. Through the prototype fabrication and experimental validation, the results indicate that the weld recognition rate reaches 98.94%, and the complete weld recognition rate reaches 97.87%, the gray-level co-occurrence matrix feature extraction recognition method demonstrates good weld recognition rates and detection performance in small-diameter clean pipelines.The proposed method has a higher average accuracy over time compared to other methods, and thus possesses good application value.