In big data services, duplicate removal of public opinion information is inevitable, and it lacks theoretical guidance. There is a research on the classical duplicate removal algorithm such as SimHash, MinHash, Jaccard, Cosine Similarty, as well as common segmentation algorithm and feature selection algorithm in order to seek excellent performance of the algorithm. The Jaccard based on short article and the SimHash algorithm based on Cosine Distance are proposed to improve the traditional algorithms. Aiming at the problem of the low efficiency of experiment on many research subjects, the strategy is adopted that filters out algorithm of obvious advantages by vertical comparison firstly, and gets the most appropriate algorithm collocation by horizontal comparison secondly, at last, makes a comprehensive comparison. The experiment of 3000 public opinion samples shows that improved SimHash has better effect than traditional SimHash; improved Jaccard increases the recall rate by 17% and improves the efficiency by 50% compared with traditional Jaccard. Under the condition that the accuracy is higher than 96%, MinHash+Jieba full pattern word segmentation and Jaccard+IKAnalyzer intelligent word segmentation has more than 75% recall rate and good stability. MinHash is a bit weak than Jaccard in the aspect of removal effect, yet has the best comprehensive performance and shorter feature comparison time.