The　goal　of　relation　classification　is　to　recognize　the　relationship　between　two　marked　entities　in　a　sentence.　It　is　a　crucial　constituent　in　natural　language　processing.　Up　till　the　present　moment,　most　previous　neural　network　models　for　this　task　either　focus　on　using　the　handcrafted　syntactic　features　or　learning　semantic　representations　of　raw　word　sequences,　they　have　no　capacity　for　encoding　the　whole　sentence　representation　including　syntax　and　semantic　information.　In　general,　information　of　syntax　and　semantics　can　both　have　significant　effect　on　classifying　relation.　Based　on　this　idea,　we　propose　a　novel　two-channel　neural　network　architecture　with　attention　mechanism　in　the　paper　to　handle　this　task.　First,　we　employ　bidirectional　sequence　long　short-term　memory　(LSTM)　channel　to　capture　the　semantic　information　and　acquire　syntactic　knowledge　by　utilizing　tree　structure　LSTM　channel.　Second,　sentence-level　attention　mechanism　for　word　sequences　is　used　to　determine　which　parts　of　the　sentence　are　most　influential　component.　Eventually,　we　conduct　experiments　on　two　real-world　datasets:　the　Wikipedia　and　the　SemEval-2010　Task8　dataset.　The　experimental　results　on　datasets　demonstrate　that　our　method　can　make　better　use　of　the　information　contained　in　sentences　and　achieves　impressive　improvements　on　relation　classification　as　compared　with　the　existing　methods.