Concerning the problems of the difficulties in obtaining， the limitation of labels， and the high cost of labeling of shale gas reservoir data， a Multi-standard Active query Multi-label Learning （MAML） algorithm was proposed. First of all， with the consideration of the informativeness and representativeness of the samples， the preliminary processing was performed on the samples. Secondly， the sample richness constraints including attribute differences and label richness were added， on this basis， the valuable samples were selected and the labels of these samples were queried. Finally， a multi-label learning algorithm was used to predict the labels of the remaining samples. Through experiments on eleven Yahoo datasets， the MAML algorithm was compared with popular multi-label learning algorithms and active learning algorithms， and the superiority of the MAML algorithm was proved. Then， the experiments were extended to four real shale gas well logging datasets. In these experiments， compared with the multi-label learning algorithms： Multi-Label Multi-Label K-Nearest Neighbor （ML-KNN）， BackPropagation for Multi-Label Learning （BP-MLL）， multi-label learning with GLObal and loCAL label correlation （GLOCAL） and active learning by QUerying Informative and Representative Examples （QUIRE）， the MAML algorithm improved the average prediction accuracy of comprehensive quality of shale gas reservoirs by 45 percentage points， 68 percentage points， 68 percentage points， and 51 percentage points， respectively. The practicability and superiority of the MAML algorithm in the prediction of shale gas reservoir sweet spots are fully proved by these experimental results.