Due to the loose organization of crowdsourcing, crowdsourcing workers have deceptive behaviors in the process of completing tasks. How to identify the deceptive behaviors of workers and reduce their impact, thus ensuring the completion quality of crowdsourcing tasks, has become one of the research hotspots in the field of crowdsourcing. Based on the evaluation and analysis of the task results, a Weight Setting Algorithm Based on Generalized Pareto Distribution (GPD) (WSABG) was proposed for the unified type deceptive behaviors of crowdsourcing workers. In the algorithm, the maximum likelihood estimation of GPD was performed, and the dichotomy was used to approximate the zero point of the likehood function in order to calculate the scale parameter σ and shape parameter ε. A new weight formula was defined, and an absolute influence weight was given to each worker according to the feedback data of the crowdsourcing workers to complete the current task, and finally the GPD-based crowdsourcing worker weight setting framework was designed. The proposed algorithm can solve the problem that the difference between the task results data is small and the data are easy to be centered on the two poles. Taking the data of Yantai University students' evaluation of teaching as the experimental dataset, with the concept of interval transfer matrix proposed, the effectiveness and superiority of WSABG algorithm are proved.

The current mainstream sequence labeling is based on Recurrent Neural Network (RNN). Aiming at the problem of RNN and sequence labeling, an improved multilayer Bi-direction Long Short Term Memory (BLSTM) network for sequence labeling was proposed. Each layer of BLSTM had an operation of information fusion, and the output contained more contextual information. In addition, a method to perform Chinese word segmentation and punctuation prediction jointly was proposed. Experiments on the public datasets show that the improved multilayer BLSTM network model can improve the classification accuracy of Chinese segmentation and punctuation prediction. In the case of two tasks that need to be accomplished, the joint task method can greatly reduce the complexity of the system, and the new model and the joint task method can also be applied to solve other sequence labeling problems.

In big data environment, the research on mobile crowdsourcing system has become a research hotspot in Mobile Social Network (MSN). However, the selfishness of individuals in networks may cause the distrust problem of mobile crowdsourcing system. In order to inspire individuals to select trustful strategy, an online incentive mechanism based on reputation for mobile crowdsourcing system named RMI was proposed. Combining evolutionary game theory and Wright-Fisher model in biology, the evolution trend of mobile crowdsourcing system was studied. To solve free-riding and false-reporting problems, the reputation updating methods were established. Based on the above researches, an online incentive mechanism was built, which can inspire workers and requesters to select trustful strategies. The simulation results verify the effectiveness and adaptability of the proposed incentive mechanism. Compared with the traditional social norm-based reputation updating method, RMI can improve the trust degree of mobile crowdsourcing system effectively.