This article proposed a graphical and statistical features-based broad learning system (GSF-BLS) to detect tropical cyclogenesis with the cross-calibrated multiplatform version 2.0 (CCMP V2.0) wind products. The framework of the proposed model is composed of three modules: the data preprocessing module, the feature extraction module, and the basis broad learning system (BLS). At the stage of data preprocessing, we use the CCMP V2.0 data to match the best tracks and the global tropical cloud cluster (TCC) tracks to obtain the developed and undeveloped samples. At the feature extraction stage, a convolution module with pretrained weights is used to extract the graphical features (GFs). Meanwhile, the statistical features (SFs) are calculated based on the divided subregions of each sample. Thus, the combination of these GFs and SFs forms the input vectors. Then, the training time of GSF-BLS on CPU is only 1/20th of that of deep learning models, showing its simplicity and efficiency in model training. The overall accuracy, probability of detection (POD), and false alarm rate (FAR) on the testing set are 89.46%, 86.78%, and 8.31%, respectively. More importantly, the incremental learning ability of GSF-BLS makes it superior to most deep learning models in model updating, which can avoid the computational burden caused by retraining. Finally, the case study results show that GSF-BLS can predict tropical cyclogenesis in 52 of 70 cases in advance, and the average lead times are 13.54 h. Therefore, the experimental results demonstrate that GSF-BLS is a promising tropical cyclogenesis detection model.