从2002年的SARS到2014年的埃博拉，病毒性传染病严重危害着人类的健康和国家社会的发展。但由于病毒基因的变异速度快，使得传染病的预防具有滞后性，所以如何利用已知的病毒基因序列来提前获得未知病毒的基因序列，从而进行提前预防便是传染病研究的重点。生物上，多数是采用基因重组技术从而获得新的病毒基因序列，但整个实验过程耗时耗力，因此不能进行快速大量的预测。本项目基于现有的病毒基因库，利用自然向量法将对未知病毒的基因序列的预测转化为数学问题，从而利用大数据运算算法对其进行解决，并且基于提出的算法建立一个传染病预测平台。相比于生物方法，本项目致力于利用大数据计算平台对未知病毒基因序列进行预测，大大节约了时间与人力，同时可以对生物实验进行辅助，从而更精确更快速的对病毒性传染病进行提前预防。

From the SARS outbreak in 2002 to Ebola in 2014, viral infectious diseases seriously endanger human health and the development of the country. However, the mutation of viral genes makes the prevention of infectious diseases not timely enough. so how to make use of the known virus gene sequences to find the new virus gene sequences is the key to prevent infectious disease in advance. Biological experiment always use gene recombination techniques to obtain new sequences of viral genes, but the whole process was time-consuming and could not be predicted quickly. This project is based on the existing virus gene library, and uses the natural vector method to transform the prediction of the gene sequence of unknown virus into a mathematical problem, so that it can be solved by the large data computing algorithm. Compared to the biological method, this project is committed to using a large data computing platform to predict gene sequence of unknown virus. This algorithm greatly saves manpower and time, and can be used in biological experiments, thus more accurate for viral infectious diseases prevention in advance.