Traditional information extraction method based on Conditional Random Field (CRF) requires large-scale labeled corpus, it is expensive to label corpus manually and the extraction precision is low in processing agriculture-related product name extraction and category labeling. In order to solve this problem, a method of agriculture-related product name extraction and category labeling based on agricultural ontology and CRF was proposed, automatic extraction and classification of agriculture-related product names was regarded as sequence labeling. Firstly, original data was processed, word, part of speech, geographical attributes and ontology concept features were selected. Then, parameters of the CRF model were trained by the improved quasi-Newton algorithm and decoding was implemented by Viterbi algorithm. A total of four groups of comparative experiments were completed and seven categories were identified. CRF, Hidden Markov Model (HMM) and Maximum Entropy Markov Model (MEMM) were compared through experiments. Finally, the supply and demand trend analysis of agriculture produce was accomplished. The experimental results show that the overall precision, recall and F-score of the open test were increased by 10.20%, 59.78% and 37.17% respectively by adding ontology concepts with appropriate CRF features; it also proves the feasibility, effectiveness and practical significance of the method in promoting automatic supply and demand docking of agricultural products.