Objective　Yanqi　Basin　is　an　important　production　base　of　characteristic　agricultural　products　in　Xinjiang,　and　the　planting　structure　of　crops　is　complicated.　In　this　study,　the　time　series　remote　sensing　data　were　used　to　classify　and　identify　crops　in　the　study　area,　so　as　to　obtain　the　spatial　distribution　of　different　crops　and　their　planting　areas,　which　were　the　important　basis　for　government　sectors　to　formulate　grain　policies　and　economic　plans.　At　the　same　time,　the　applicability　of　time-weighted　dynamic　time　warping　(TWDTW　)　method　in　crop　classification　and　the　application　potential　of　GF-1　WFV　in　agriculture　were　also　discussed.　Method　The　normalized　vegetation　index　(NDVI),　calculated　from　the　2018　time　series　GF-1　WFV　data　set　in　Yanqi　Basin,　Xinjiang,　was　used　to　study　the　crops　recognition　based　on　TWDTW　method.　Sample　points　of　different　crops　were　collected　to　form　standard　sequence　of　NDVI　for　each　crop.　The　TWDTW　similarity　matching　algorithm　was　used　to　calculate　the　similarity　distance　between　each　pixel　to　be　classified　and　the　standard　sequence　of　different　crops.　The　smaller　the　distance　was,　the　higher　the　similarity　was.　The　similarity　was　used　to　determine　the　crop　type　of　the　pixel,　and　the　final　classification　result　was　obtained.　At　the　same　time,　the　classification　rules　of　decision　tree　were　established　according　to　the　NDVI　curve　of　time　series,　and　the　classification　result　was　obtained　by　manually　setting　the　classification　threshold,　and　compared　with　that　of　the　TWDTW　method.　Result　The　classification　results　of　the　two　methods　were　very　consistent.　Peppers　were　the　most　widely　planted　and　the　wheat　was　mainly　distributed　in　the　northern　part　of　the　Yanqi　Basin　and　the　21st　Division　of　the　Second　Agricultural　Division.　The　distributions　of　tomato　and　sugar　beet　were　relatively　sporadic.　Among　the　results　of　planting　area,　pepper　had　the　largest　planting　area,　followed　by　tomato,　wheat　and　sugar　beet.　The　accuracy　of　the　classification　results　of　the　TWDTW　and　decision　tree　methods　was　verified　by　the　field　sample　points:　The　overall　accuracy　of　them　were　89.58%　and　90.97%,　respectively,　and　the　kappa　index　of　them　were　0.804　and　0.830,　respectively.　The　classification　accuracy　of　the　TWDTW　method　was　slightly　higher　than　that　of　the　decision　tree　method.　Conclusion　Compared　with　the　decision　tree　classification　method,　the　classification　accuracy　of　the　TWDTW　method　was　slightly　improved,　the　classification　result　was　more　objective　and　reliable.　The　algorithm　of　TWDTW　method　was　not　limited　by　geographical　factors　and　had　strong　flexibility　and　applicability.　The　experimental　results　showed　that　using　TWDTW　algorithm　to　identify　crops　based　on　the　GF-1　WFV　data　set　of　dense　temporal　phase　could　get　better　classification　results,　and　it　had　great　application　and　popularization　value　in　agricultural　field.　©　2019　Editorial　Department　of　Scientia　Agricultura　Sinica.　All　rights　reserved.