The　pump　provides　the　necessary　pressure　and　flow　for　the　organic　Rankine　cycle　(ORC)　system.　The　traditional　methods　have　obvious　limitations　when　analyzing　the　time-varying　characteristics　of　the　key　operating　parameters　of　the　pump.　This　study　first　introduces　the　scatter　plot　analysis　method　to　analyze　and　evaluate　the　time-varying　and　coupling　characteristics　of　the　hydraulic　diaphragm　metering　pump.　Then,　a　machine　learning-fitting　algorithm　hybrid　model　is　constructed　to　solve　and　verify　the　actual　matching　correlation　equation　of　the　key　operating　parameters.　In　addition,　the　complicated　non-linear　relationship　brings　great　challenges　to　obtaining　the　limit　value　of　the　pump　isentropic　efficiency.　This　study　introduces　the　bilinear　interpolation　algorithm　to　systematically　analyze　the　change　trend　between　operating　parameters　and　isentropic　efficiency.　Based　on　the　wavelet　neural　network　(WNN)　with　momentum　term　and　particle　swarm　optimization-adaptive　inertia　weight　adjusting　(PSO-AIWA),　a　machine　learning　framework　with　an　intelligent　algorithm　is　constructed.　Under　this　framework,　the　maximum　isentropic　efficiency　of　the　pump　can　be　stabilized　at　70.22-74.67%　under　all　working　conditions.　Through　the　theoretical　analysis　model,　the　effectiveness　of　this　framework　is　evaluated.　Finally,　the　optimal　cycle　parameters　are　evaluated.　This　study　can　provide　direct　significance　for　the　analysis　and　optimization　of　the　actual　performance　of　the　ORC　system.