Segmentation of brain tumors in magnetic resonance imaging (MRI) is a difficult job due to the delineation data shortage, class imbalance, and significant differences among private databases. To solve those problems, we propose an adaptive local-global information learning (ALGIL) segmentation algorithm. This method only requires a small amount of delineation data, which solves the problem of traditional supervised learning depending on the amount of delineation data. The spatial-domain and frequency-domain information of the image is fused to convert the image from the spatial domain to the frequency domain through wavelet transform, and statistical features and texture features are extracted from the low-frequency and high-frequency sub-bands respectively. The limitations of traditional single-domain feature extraction are thereby resolved. With the ALGIL segmentation algorithm, we construct a similarity matrix by weighting the image according to the feature weights obtained through the random forest algorithm. Then, an exponential decay function is employed to adaptively adjust the degree of influence of the labeled samples on the algorithm. In this way, we solve the problem of poor segmentation results caused by the delineation data shortage. The experimental results of the proposed method on the public data set Brats2018 show that compared with other advanced models, the method has greatly improved the efficiency of image segmentation by enhancing various evaluation indicators and reducing the need for delineation samples. It provides a new idea for the automatic and accurate segmentation of brain gliomas.