The process of collecting target image data for intelligent manufacturing production lines is affected by noise, changes in lighting, and other issues, resulting in poor quality of input images, which in turn affects the accuracy of target detection. Therefore, a target detection method for intelligent manufacturing production lines based on 3D laser scanning technology is designed. Firstly, a 3D laser scanner is used to obtain the point cloud data of the target to be detected, and the topological relationship between the data is calculated using point cloud transformation criteria to generate a complete 3D laser image. Then, hybrid wavelet transform is used to denoise the 3D laser target image. Finally, four parameters, energy, entropy, contrast, and correlation, are used to extract image texture features and normalize them to create the optimal classification function. Support vector machine algorithm is used to partition the target sample image data of the production line, completing the intelligent manufacturing production line target detection work. The experimental results show that the proposed method has a maximum intersection to union ratio of 0.97 and an F1 value of 0.96, with an average detection time of only 0.53 seconds. This indicates that the proposed method has high detection accuracy, fast efficiency, strong robustness, and considerable usability in practical operations, providing technical support for the intelligent manufacturing industry.