Abstract: Abstract: Chrysanthemum is one of the top 10 traditional famous flowers in China, which has significant importance and great ornamental value and medicinal value. The chrysanthemum flowers are characterized by a large number of varieties and a wide range of petal shapes, which pose big challenges to their intelligent identification and efficient management. Currently, the identification and management of chrysanthemum mainly relies on the traditional manual way, and as a result, the efficiency is quite low. At the contemporary era, deep learning as a powerful technique in artificial intelligence field is becoming a prevalent way of identification and classification on text, image, video, and so on. Based on the end-to-end convolutional neural network deep neural network directly acting on the original chrysanthemum image dataset, this paper aims at obtaining the characteristic information of chrysanthemums through the multi-layer neural network. By this means, the problem of extracting the features manually is avoided, and then optimization target function is applied to achieve a better image recognition accuracy. Based on this, the system of chrysanthemum flower pattern intelligent recognition and breed classification is researched and implemented. In view of the subtle differences among the flower patterns of chrysanthemums, on the one hand, in order to preserve as much information as possible for the data, tensor is employed to represent the image data; on the other hand, the pairwise confusion loss function based on pair similarity is used to distinguish pattern differences and similarities. By this means, the objective function of distinguishing the different flower patterns is realized on the fine grain size. The system not only can identify the chrysanthemum pattern, but also can give the probability value of the top 3 results. In addition to this, the variety information covered by the flower pattern is also provided. The operation of the system can be divided into 2 stages: the off-line training and the online classification. Off-line models can be hosted in the cloud environment such as Amazon AWS for the easy usage on the mobile platform. Moreover, the model can be replanted and updated with little hindrance. In order to train the network model, we collected a large amount of data of real chrysanthemum image, and manually marked the relevant pattern and category information of chrysanthemum. Based on the datasets, we conducted extensive experiments with our system and made comparisons with 3 existing systems, and experimental results show that: The identification accuracy of the system has been significantly improved compared with the existing systems for chrysanthemum flower pattern. Beyond that, the system can provide more detailed chrysanthemum species information at the same time. The average recognition rate can reach about 0.95, and even surpass the rate of 0.98 for some chrysanthemum patterns. The system provides a powerful means for the automatic management of chrysanthemum and fills the gaps in chrysanthemum pattern recognition and classification. In this paper, the research on the intelligent identification and effective management of chrysanthemum flower pattern has great significance in theory and practice.