The　COVID-19　pandemic　spreads　rapidly　around　the　world,　and　has　given　rise　to　huge　impacts　on　all　aspects　of　human　society.　This　study　utilizes　big　data　techniques　to　analyze　the　impacts　of　COVID-19　on　the　user　behaviors　and　environmental　benefits　of　bike　sharing.　In　this　study,　a　novel　method　is　proposed　to　calculate　the　trip　distances　and　trajectories　via　a　python　package　OSMnx　so　as　to　accurately　estimate　the　environmental　benefits　of　bike　sharing.　In　addition,　we　employ　the　topological　indices　arising　from　complex　network　theory　to　quantitatively　analyze　the　transformation　of　user　behavior　pattern　of　bike　sharing　during　the　COVID-19　pandemic.　The　results　show　that　this　pandemic　has　impacted　the　user　behaviors　and　environmental　benefits　of　bike　sharing　in　Beijing　significantly.　During　the　pandemic,　the　estimated　reductions　of　energy　consumption　and　emissions　on　6th　Feb　decreased　to　approximately　1　in　17　of　those　on　a　normal　day,　and　the　environmental　benefits　at　most　recovered　to　70%　of　those　in　normal　days.　The　impacts　of　COVID-19　on　the　environmental　benefits　in　different　districts　are　different.　Furthermore,　the　decline　of　average　strength　and　strength　distribution　obeying　exponential　distribution　but　with　different　slope　rates　suggests　that　people　are　less　likely　to　take　bike　sharing　to　the　places　where　were　popular　before.　The　pandemic　has　also　increased　the　average　trip　time　of　bike　sharing.　Our　research　may　facilitate　the　understanding　of　the　impacts　of　COVID-19　pandemic　on　our　society　and　environment,　and　also　provide　clues　to　adapt　to　this　unprecedented　pandemic　so　as　to　respond　to　similar　events　in　the　future.