The　inherent　sparsity　of　electronic　medical　record　(EMR)　poses　difficulties　for　the　learning　of　patient　similarity.　Graph-based　modeling　methods　can　infer　missing　values　by　learning　complex　relations　among　medical　facts,　which　are　becoming　the　mainstream　options　for　patient　similarity　analysis.　However,　existing　graph-based　solutions　mainly　focus　on　general　patterns　among　patients　and　overlook　local　differences,　potentially　losing　semantic　information　on　patient　similarity.　Additionally,　in　real　medical　situations,　entities　related　to　symptoms　or　treatment　types　often　involve　multiple　diseases,　which　provide　erroneous　signals　in　similarity　assessment.　Therefore,　this　paper　proposes　a　novel　deep　learning　method　called　Multi-view　Hierarchical　Learning　Network　(MHLN)　for　patient　similarity　measurement.　This　method　extracts　dependency　information　between　entities　from　both　local　and　global　perspectives.　Additionally,　it　assigns　different　importance　levels　to　different　types　of　medical　entities,　enabling　the　learning　of　dependency　features　between　entity　representations　through　local　and　global　encoders　to　generate　representative　embeddings　for　similarity　computation.　Finally,　we　evaluate　MHLN　on　real-world　Chinese　EMR　data,　and　the　results　demonstrate　the　effectiveness　of　MHLN　compared　to　related　work.