Relation　extraction　plays　an　important　role　in　many　natural　language　processing　tasks,　such　as　knowledge　graph　and　question　answering　system.　Up　to　the　present,　many　of　the　former　relation　methods　work　directly　on　the　raw　word　sequence,　so　it　is　often　subject　to　a　major　limitation:　the　lack　of　semantic　information,　which　leads　to　the　problem　of　the　wrong　category.　This　paper　presents　a　novel　method　to　extract　relation　from　Chinese　agriculture　text　by　incorporating　syntactic　parsing　feature　and　word　embedding　feature.　This　paper　uses　word　embedding　to　capture　the　semantic　information　of　the　word.　On　the　basis　of　the　traditional　method,　this　paper　integrates　the　dependency　parsing,　the　core　predicate　and　word　embedding　features,　using　the　naive　Bayes　model,　support　vector　machine　(SVM)　and　decision　tree　to　build　experiment.　We　use　the　websites　knowledge　base　to　construct　a　dataset　and　evaluate　our　approach.　Experimental　results　show　that　our　proposed　method　achieves　good　performance　on　the　agriculture　dataset.　The　dataset　and　the　word　vectors　trained　by　Word2Vec　are　available　at　Github　(https://github.com/A-MuMu/agriculture.git　).　©　2018,　Springer　Nature　Switzerland　AG.