The sunspot drawings around the globe provide long historical records for understanding the long-term trends in solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named as SPR-Mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPRMask consists of three parts: backbone, shared head and mask branch. Especially, it adopts a scale-aware attention network (SAAN) and a PointRend module in the mask branch to improve the accuracy of target edge segmentation. Besides that, each sunspot belonging to northern or southern (N-S) hemisphere is determined by transforming its cartesian coordinates to spherical coordinates after extracting P, B0 and L0 handwritten in sunspot drawings using a revised Lenet-5 deep learning method. The precision, recall and AP of SPR-Mask are 0.92, 0.93, and 0.92, respectively. The test results show the SPR-Mask method has a good performance. The numbers and areas of pores, spots, umbrae and penumbrae for N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelh¨ohe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show they have similar trends, high correlations and similar N-S asymmetries. All data of YNAO are public shared at https://github.com/yzs64/YNAO sd/, which are abundant complementary to the other sunspot catalogues in the world.