Entity　alignment　aims　at　associating　semantically　similar　entities　in　knowledge　graphs　from　different　sources.　It　is　widely　used　in　the　integration　and　construction　of　professional　medical　knowledge.　The　existing　deep　learning　methods　lack　term-level　embedding　representation,　which　limits　the　performance　of　entity　alignment　and　causes　a　massive　computational　overhead.　To　address　these　problems,　we　propose　a　Siamese-based　BERT　(SiBERT)　for　Chinese　medical　entities　alignment.　SiBERT　generates　term-level　embedding　based　on　word　embedding　sequences　to　enhance　the　features　of　entities　in　similarity　calculation.　The　process　of　entity　alignment　contains　three　steps.　Specifically,　the　SiBERT　is　firstly　pre-trained　with　synonym　dictionary　in　the　public　domain,　and　transferred　to　the　task　of　medical　entity　alignment.　Secondly,　four　different　categories　of　entities　(disease,　symptom,　treatment,　and　examination)　are　labeled　based　on　the　standard　terms　selected　from　standard　terms　dataset.　The　entities　and　their　standard　terms　form　term　pairs　to　train　SiBERT.　Finally,　combined　with　the　entity　alignment　algorithm,　the　most　similar　standard　term　is　selected　as　the　final　result.　To　evaluate　the　effectiveness　of　our　method,　we　conduct　extensive　experiments　on　real-world　datasets.　The　experimental　results　illustrate　that　SiBERT　network　is　superior　to　other　compared　algorithms　both　in　alignment　accuracy　and　computational　efficiency.