The　prediction　of　energy　consumption　is　important　for　the　efficient　operation　of　building　air-conditioning　systems.　Most　predicted　models　are　based　on　historical　energy　consumption　data　and　the　factors　influencing　air　conditioning　systems,　including　weather,　time　of　day,　and　previous　consumption.　However,　the　traditional　prediction　models,　such　as　the　Autoregressive　Integrated　Moving　Average　(ARIMA)　time　series　model　and　back　propagation　(BP)　neural　network　model,　show　large　errors　in　their　prediction　of　the　energy　consumption　of　air-onditioning　systems.　To　achieve　better　prediction,　the　Long　Short-Term　Memory　(LSTM)　model　of　deep　learning　is　adopted　in　this　study　based　on　an　air-conditioning　system　of　a　University　Library　in　Guangzhou.　The　results　demonstrate　that　the　LSTM　model　can　produce　more　reliable　predictions.　The　daily　energy　consumption　forecast　reduced　by　11.2　%　compared　to　that　of　the　Autoregressive　Moving　Average　model　(MAPE).　The　hourly　energy　consumption　forecast　reduced　by　16.31　%.　In　addition,　compared　with　the　BP　neural　network　model,　the　MAPE＇s　daily　energy　consumption　prediction　reduced　by　49　%　and　the　hourly　energy　consumption　prediction　reduced　by　36.61　%.