In the traditional speech recognition system， the optimal decoding paths are determined by a language model restrained by the training data. Almost inevitably， the right pronunciation may produce wrong character recognition results in some scenarios. In order to use the prosodic information in speech to enhance the probability of correct character combination in language model， an end-to-end speech recognition method based on prosodic features was proposed. Based on the attention mechanism based encoder-decoder speech recognition framework， firstly， the coefficient distribution of attention mechanism was used to extract prosodic features such as pronunciation interval and pronunciation energy. Then， the prosodic features were combined with decoder to significantly improve the accuracy of speech recognition in the cases with the same or similar pronunciation and semantic ambiguity. Experimental results show that the proposed method achieves a relative accuracy improvement of 5.2% and 5.0% respectively compared with the baseline end-to-end speech recognition method on 1 000 h and 10 000 h speech recognition tasks and improves the intelligibility of speech recognition results.

To make use of the social relationship between experts and to consider the limited rationality of decision-making experts in group decision-making， a TODIM （TOmada de Decis?o Interativa Multicritério） group decision-making method under trust network was proposed. Firstly， according to the number of discussions of the experts， in each discussion， each expert would refer to his/her trustee’s decision matrix according to the degree of trust acceptance， and the decision matrices would be modified through information interaction and negotiation. Then， when the set number of expert discussions was met， the final group decision-making matrix was calculated. Finally， the TODIM group decision-making method under trust network and TODIM group decision-making method were applied to calculate the ranking results of different schemes. The ranking results were compared and analyzed， and the sensitivity analysis was performed on the number of expert discussions and trust acceptance. The case analysis results show that the TODIM group decision-making method under trust network can fully integrate trust network， ensure the multi-stage information interaction and feedback process in the decision-making process， and is superior to the general TODIM group decision-making method in comparison analysis and sensitivity analysis.