The classification and detection of fresh cut rose is of great significance to its sales. At present, the classification and detection of fresh cut rose is mainly manual. In order to reduce the loss of fresh cut rose flowers in the process of manual classification, a set of classification and detection system of fresh cut rose flowers was built based on machine vision method and Halcon software. Firstly, the experimental platform was designed and the classification standard of fresh cut rose flowers was established. Then, image enhancement and data enhancement technology are added to improve the image effect, increase the number of samples, and use the median filter method to eliminate the image noise, so as to ensure the accuracy of classification results. Finally, the training samples are added to five models for training, and the training results of each model are compared. Mobilenet_v2 model is selected to join the image classification system to classify the top view of fresh cut flowers, and a one-dimensional measurement system is established to measure the length of flower stems; Establish the evaluation criteria model to complete the classification of fresh cut rose flowers. After testing, the classification accuracy of the top view classification system is 94%, and the flower stem length measured by one dimension is within the error range.