In China, the recognizing whether a website belongs to science websites relies mainly on expert judgment to proceed. This kind of subjective judgment is not only time-consuming, and the results are not reliable. Browsing and judging by experts have low efficient because the rich website content. They only can process very limited part of any website under certain time and energy. Besides this, different people may make different judgments. It is necessary to propose a quantitative method based on machine intelligence. This paper will discuss the feature word vectors of Chinese popular science websites what is processed by computer abstracted from real content based on vector space model. We think it can better the performance of the site's textual content and meaning. Based on this method, people may make a system to automatically determine the ultimate realization of website content if it contains science ingredients as well as what kind of science content.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |