In the industrial field, Quick Response code has been widely used in the automatic management of production lines because of its high-density storage and fast reading characteristics. However, in real-world scenarios, the recognition success rate of Quick Response code often falls short of the desired outcome due to interference from factors such as lighting, noise, and scale variations. To solve this problem, this paper proposes an Image Enhancement-based Quick Response Code Recognition in complex working conditions (IE-QRCR). Firstly, the Quick Response code image is preprocessed through the joint optimization method of perspective transformation and low-light enhancement to improve image quality and reduce interference. Then, the optimized Quick Response code image is input into the recognition algorithm for recognition. The method can effectively improve the accuracy and robustness of recognition, especially in complex environments with low illumination and poor image quality. We verify the effectiveness of the method on some public Quick Response code datasets, which provides a useful reference for further research in the field of Quick Response code recognition.