In the industrial environment, the positioning of mobile terminals plays an important role in production scheduling. Visible light positioning (VLP) based on a CMOS image sensor has been widely considered as a promising indoor positioning technology. However, the existing VLP technology still faces many challenges, such as modulation and decoding schemes, and strict synchronization requirements. In this paper, a visible light area recognition framework based on convolutional neural network (CNN) is proposed, where the training data is the LED images acquired by the image sensor. The mobile terminal positioning can be realized from the perspective of recognition without modulating LED. The experimental results show that the mean accuracy of the optimal CNN model is as high as 100% for the two-class and the four-class area recognitions, and is more than 95% for the eight-class area recognition. These results are obviously superior to other traditional recognition algorithms. More importantly, the model has high robustness and universality, which can be applied to various types of LED lights.