Data acquisition, text keyword extraction and text sentiment orientation analysis are important technologies which can be applied in text content analysis. In this paper, a general crawler based on Scrapy framework is designed. It can be applied to many kinds of web sites and improved the efficiency and the versatility. A mixed method based on Textrank algorithm and TF-IDF algorithm is proposed. It can be applied to mobile client to extract the keywords. For the text tendency analysis, a new method based on SnowNLP is proposed. The experiment shows thaData acquisition, text keyword extraction and text sentiment orientation analysis are important technologies which can be applied in text content analysis. In this paper, a general crawler based on Scrapy framework is designed. It can be applied to many kinds of web sites and improved the efficiency and the versatility. A mixed method based on Textrank algorithm and TF-IDF algorithm is proposed. It can be applied to mobile client to extract the keywords. For the text tendency analysis, a new method based on SnowNLP is proposed. The experiment shows that crawling and extracting results and the tendency judge are more accurate for the long educational text.Data acquisition, text keyword extraction and text sentiment orientation analysis are important technologies which can be applied in text content analysis. In this paper, a general crawler based on Scrapy framework is designed. It can be applied to many kinds of web sites and improved the efficiency and the versatility. A mixed method based on Textrank algorithm and TF-IDF algorithm is proposed. It can be applied to mobile client to extract the keywords. For the text tendency analysis, a new method based on SnowNLP is proposed. The experiment shows that crawling and extracting results and the tendency judge are more accurate for the long educational text.t crawling and extracting results and the tendency judge are more accurate for the long educational text.