Image recognition is the key to smart logistics systems. Traditional handwriting feature extraction is difficult to meet the requirements of image recognition. Deep learning is used for image recognition. Firstly, convolutional neural network (CNN) and deep Boltzmann machines under deep learning are introduced. Second, cellular neural networks are used to perform feature recognition and extraction on images. Finally, a Parzen classifier is used to classify the obtained image features. The novelty is that through the structural design and research of the intelligent logistics system, the CNN is combined to construct a management system of supply chain logistics of image recognition and information processing. The experimental results show that the recognition accuracy time of the proposed improved fusion algorithm on the Mixed National Institute of Standards and Technology data set is 198.85 s. When the improved algorithm achieves the same recognition accuracy, it takes 159.65 s. The recognition efficiency of the improved algorithm is 19.71% higher than that of the unimproved algorithm. In addition, when the unimproved algorithm reaches the maximum number of iterations, the error rate is 2.47%. The error rate of the improved algorithm is only 0.74%. This study provides a basis for improving the image recognition accuracy and has certain practical value.