Sunspot drawings around the globe provide long historical records for understanding the long-term trends in the solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes to the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named SPR-mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPR-mask consists of three parts: backbone, shared head and mask branch. It especially 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 the 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 the N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelhohe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show similar trends, high correlations, and N-S asymmetries. All data of YNAO are publicly shared at https://github.com/yzs64/YNAO_sd/, which are abundant and complementary to the other sunspot catalogs in the world.