To address the issue of neglecting workers’ collaborative relationships in traditional collaborative crowdsourcing task allocation， a collaborative crowdsourcing task allocation method fusing community detection was proposed， by considering the social and historical cooperative relationships among workers. Firstly， potential social relationships among crowdsourced workers were mined by a community detection algorithm to establish candidate communities. Secondly， after defining factors such as degree of collaboration， interaction cost， and utility of task allocation， a model for collaborative crowdsourcing task allocation was developed by considering skill coverage， credibility， and budget comprehensively. Thirdly， the strategies such as Piece-Wise chaotic mapping， inverse cumulative function operator based on Cauchy distribution， adaptive tangent flight operator， and sparrow warning mechanism were introduced and an optimized Sand Cat Swarm Optimization （SCSO） algorithm — TSCSO was proposed. Finally， TSCSO algorithm was used to solve the aforementioned model. Experimental results on examples synthesized from real datasets of different scales demonstrate that the proposed algorithm has the task allocation success rate of at least 90%. Furthermore， TSCSO algorithm improves the average task allocation utility ranging by 20.08% to 53.38% compared to other optimized intelligent algorithms， verifying the proposed algorithm’s applicability， stability， and efficacy in collaborative crowdsourcing task allocation problems.

Chinese comparative sentences are more focused in the field of linguistics. Using machine learning methods to identify comparative sentences, however, has only just started. According to the basic principle of the association rules mining algorithm, a method of comparative sentences based on the associated feature vocabulary was proposed. This method regarded word and part of speech as basic elements, defined the connecting way between the table definition core words and interdependent relationship words, and used the Support Vector Machine (SVM) classifier for the identification of comparative sentences. The experimental results show that this method can effectively identify Chinese comparative sentences, and achieves good results in precision, recall and F-measure.