Knowledge-driven　GIS　increasingly　requires　multi-source,　multi-type,　and　multi-model　crowd-sensing　spatiotemporal　data,　whose　data　quality　is　difficult　to　guarantee　and　determine.　Hence,　extracting　quality　indicator　information,　widely　present　in　various　unstructured　web　texts,　is　crucial　to　providing　supplementary　quality　information　for　crowd-sensing　spatiotemporal　data.　Recent　advances　in　large　language　models　show　potential　in　extracting　quality　indicator　information.　However,　it　is　still　hard　to　get　accurate　results　from　large　language　models　that　use　different　quality　indicators　for　crowd-sensing　spatiotemporal　data.　Therefore,　we　have　designed　a　large　language　model　that　is　fine-tuned　for　the　extraction　of　spatiotemporal　quality　information　from　quality　description　text　(LLMFT-STQIE).　Firstly,　we　establish　a　quality　indicator　vocabulary　to　determine　whether　the　text　includes　quality　indicator　information　from　the　spatiotemporal　data.　Then,　we　create　a　two-stage　prompt　model　with　QILE　and　QIVE　prompts　that　include　input　text,　task　type,　instructions,　the　quality　indicator　vocabulary,　output　format,　and　a　reference　case.　This　model　is　based　on　the　fine-tuning　technology　of　large　language　models.　The　results　show　that　our　LLMFT-STQIE　achieves　an　accuracy　of　91%　and　a　recall　rate　of　80%,　respectively,　representing　improvements　of　23%　and　38%　compared　to　untuned　large　language　models.　These　results　further　show　that　the　suggested　method　easily　and　accurately　extracts　quality　indicator　information　from　web　texts　for　crowd-sensing　spatiotemporal　data.　The　study　helps　investigate　strategies　for　optimizing　huge　language　models　for　specific　scenarios　or　task　specifications.