太阳黑子是太阳活动的重要外在特征，且与诸多其他太阳活动指标均有良好的相关性。黑子数据在太阳活动，空间环境日地关系及地球气候等方面均有广泛地应用。国际太阳黑子相对数作为各类黑子数据中观测时间最长的时间序列，在诸多研究中均有十分重要的作用。2015年，国际太阳黑子相对数经过校订，公布了新版本的数据。新旧版本的数据存在诸多差别，而这些差别可能会对使用该数据的研究方法造成一定的影响。我们对新旧数据进行对比，简述了两者的差别并说明了原因和来源，并且以已有研究为例，具体讨论了数据更替对研究工作的影响。 太阳黑子的预测是太阳黑子研究的一个重要方向。由于数据的更替，现有的基于旧数据的预测方法大多需要更新甚至修改。因此，基于新数据，我们提出了一种新的预测方法。该方法使用了一个四参数的修正高斯函数来描述各个活动周的黑子数变化，通过对四个参数的预测得到预测周期的变化曲线。该方法给出的两种不同的预测峰值分别为109和113，实际峰值为116；给出的峰值位置分别为第54个月和第57个月，位于实际曲线的两个峰值（第39个月和第64个月）之间。我们使用了第22、23和24周的数据对我们的方法进行了验证，结果表明该方法无偏性和一致性良好，同时具有普适性。 黑子相对数在地球相关的研究中也十分重要作用。2011年，车涛等人利用SMMR，SSMI和AMSR-E的卫星亮温度数据生成了中国0.25度长时间序列雪深数据集。我们对雪盖数据进行提取，使用滑动平均对雪盖的周年相进行滤波，再使用小波相干等方法将其与太阳活动时间序列进行相干分析。结果表明，不论是黑子相对数序列还是总辐射流量序列，与雪盖存在一个长度约为126个月的稳定的相干周期，对应着太阳活动的11年周期。

Sunspot is the important external characteristic of solar activity, and have well correlation with other solar activity indicators. Sunspot data are widely used in studies of solar activity, space environment, solar-terrestrial relationship and earth climate. International sunspot relative number is the longest time series observed in various sunspot data, which plays an important role in many studies. In 2015, the international relative sunspot number was completely revised and the new version of the data was published. There are many differences between the old and new versions of the data, and these differences may have considerable impact on the research based on the data. Comparing the new data with the old one, we outline the differences between the two and explain the reasons and sources of these difference. Then, With two existing research as an example, we concretely discuss the impact of data revision. The prediction of sunspot number is an important direction of sunspot research. Because of the revision of data, most of the existing prediction methods based on old data need to be updated or even modified. Therefore, based on the new data, we propose a new prediction method. The method uses a four-parameter modified Gauss function to describe the sunspot number change in each solar cycle. The predicted curve is obtained by predicting the four parameters. We used the data of 24th weeks to validate our method. The method gives two different predicted peaks with the value of 109.2 and 113.3 respectively, and the actual peak is 116.4. The predicted peak positions are 54th months and 57th months respectively, which are between the two peaks of the actual curve (39th months and 64th months). The relative sunspot number also plays an important role in earth-related research. In 2011, Che Tao et al. used SMMR, SSMI and AMSR-E satellite brightness temperature data to generate China’s 0.25 degree long time series snow depth data set. We extract snow depth changing time series from the data set, filter the annual phase of the time series, and then use the wavelet coherence method to analyze the correlation between the depth of snow cover and the time series of solar activity. The results show that, whether the sequence of sunspot relative number or total solar irradiation, there exists a stable coherent period of 126 months between the solar data and the snow depth, corresponding to the 11-year cycle of solar activity.