Along　the　way　initiated　by　Carleo　and　Troyer　[G.　Carleo　and　M.　Troyer,　Science　355　(2017)　602],　we　construct　the　neural-network　quantum　state　of　transverse-field　Ising　model　(TFIM)　by　an　unsupervised　machine　learning　method.　Such　a　wave　function　is　a　map　from　the　spin-configuration　space　to　the　complex　number　field　determined　by　an　array　of　network　parameters.　To　get　the　ground　state　of　the　system,　values　of　the　network　parameters　are　calculated　by　a　Stochastic　Reconfiguration　(SR)　method.　We　provide　for　this　SR　method　an　understanding　from　action　principle　and　information　geometry　aspects.　With　this　quantum　state,　we　calculate　key　observables　of　the　system,　the　energy,　correlation　function,　correlation　length,　magnetic　moment,　and　susceptibility.　As　innovations,　we　provide　a　high　efficiency　method　and　use　it　to　calculate　entanglement　entropy　(EE)　of　the　system　and　get　results　consistent　with　previous　work　very　well.