This research aims to solve problems arising from the trust mechanism of multimedia and its mechanism and put forward a Feedback Trust Weighted for Data Fusion algorithm (FTWDF) drawing upon the collaborative filtering algorithm and network fuzzy theory. Also, it has carried out simulation experiments and analyze its performance. It turns out that in the data reliability analysis, the transmission rate of trusted data packets of FTWDF algorithm put forward in this study is higher. In the data precision analysis, it turns out that compared to TMDF and LDTS algorithms, the correct rate under the algorithm put forward in this study is 4.1% and 8.3% higher than TMDF and LDTS. In ML100M and NF5M datasets, the FTWDF-EEFAF model yields a higher precision and thus provides better recommendation results. In the analysis of the number of death nodes, the new clustering algorithm FTWDF-EEFA model serves to increase the survival time of nodes and prolong the lifespan of networks. It improves the survival period of nodes, balances the network load and prolongs the lifespan of networks. In the analysis of energy consumption of nodes, it turns out that the FTWDF-EEFA clustering algorithm can balance the energy consumption of nodes and effectively save the overall energy of nodes. Therefore, through the study, it can be seen that improving existing algorithms serve to effectively increase lifespan of network and improve the trust mechanism. The results are as expected and it offers reference basis for the application of trust mechanism in actual network.

In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms.