The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and increasing the accuracy of semantic representation. Ultimately, the model computes the corresponding classification results by synthesizing these rich data semantic representations. Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

The diffusion of all-media content plays a vital role in guiding public opinion and ideology. However, at present, most of the media content exists on all kinds of mainstream media platforms, which poses great challenges to the effective supervision of relevant departments and society. This has led to arbitrary charges, chaotic media content, difficulties in supervision and evidence collection, and infringements of the rights and interests of original content creators. To address these problems, this paper constructs a trustworthy propagation architecture that supports multi-platform media content sharing. This architecture collaboratively builds an audio-visual blockchain through public and consortium blockchains, coupled with an improved ChinaDRM to provide digital rights management and content encryption. Simultaneously, we employ an enhanced Diffie−Hellman key agreement protocol to offer distributed encryption and decryption for media content. Within this model, various media platforms and national regulatory authorities are responsible for content storage and distribution as consortium nodes and public blockchain nodes, respectively. At the same time, users, as light nodes of public chain or service consumers of consortium blockchain, can consume and comment on content. Analysis shows that the trusted communication framework of media content based on the audio-visual blockchain has certain expansibility and practicability. It can facilitate the supervision of mainstream media platforms by national authorities and society through inter-blockchain technology, offering a novel solution for multi-platform trustworthy cooperative information sharing.