The　text-based　patent　analysis　is　grounded　in　information　extraction　technique.　However,　such　technique　suffers　from　obvious　defects　such　as　low　degree　of　automation　and　unsatisfactory　extraction　accuracy.　To　deal　with　these　problems,　after　an　information　schema　is　pre-defined,　which　contains　17　types　of　entities　and　15　types　of　semantic　relations,　a　dataset　of　1010　patent　abstracts　is　annotated　and　opened　freely　to　the　research　community.　Then,　a　novel　patent　information　extraction　framework　is　proposed,　in　which　two　deep-learning　models,　BiLSTM-CRF　and　BiGRU-HAN,　are　respectively　used　for　entity　identification　and　semantic　relation　extraction.　Finally,　to　demonstrate　the　advantages　of　the　new　framework,　extensive　experiments　are　conducted,　and　the　SAO　method　and　PCNNs　model　are　taken　as　respective　baselines　on　the　framework　and　module　levels.　Experimental　results　show　that　our　framework　out-performs　the　traditional　one　in　terms　of　automation　and　accuracy,　and　is　capable　of　extracting　fine-grained　structured　information　from　patent　texts.