Emotion-Cause Pair Extraction (ECPE) aims to identify and extract emotion clauses and their corresponding cause clauses from unannotated emotional texts. Previous approaches primarily leveraged contextual features or attention weights as semantic information to enhance feature representations. However, these methods often overlook the additional causal information that external knowledge can bring to ECPE. Given the evident inferential relationship between emotions and their causes, we propose a novel model called Rhetorical Structure Theory and Inference-Aware Graph networks (RSTIAG) to address the challenges posed by ECPE. This model constructs a semantic dependency space grounded in discourse-level rhetorical structure theory and a symbolic reasoning space based on an inference-aware graph. By integrating the introduced knowledge through a space fusion co-attention mechanism and modeling the dependencies between these two spaces, the model enables clauses to simultaneously capture diverse knowledge from both spaces. Additionally, we enrich the information dimensions of emotion-cause pairs by instantiating the reasoning process from the perspectives of causality and implicit inference. To substantiate the effectiveness of our proposed RSTIAG model, we undertake comprehensive experiments. The results demonstrate that it significantly outperforms 11 baseline models on an English benchmark corpus, confirming its effectiveness.

At present, there are many blended teaching methods relying on the “Internet + Education” model, but most of them use existing software or online platforms, which inevitably leads to the problem of “less targeted and more limited”. In addition, the data analysis for testing the effectiveness of blended learning is relatively single, which inevitably leads to the problem of insufficient persuasiveness and expressive power . Committed to solving the above problems, this article first develops a course learning assistant app based on Android studio, and implements the function of students building learning mainlines online and revisiting knowledge points offline. Secondly, in order to test the learning effectiveness, a push forward evaluation model of “Online Participation and Gain Calculation - Offline Satisfaction and Improvement Estimation (PGC-SIE)” was proposed, which comprehensively analyzes the effectiveness of blended teaching and lays the foundation for building a course brand.

The popularity of WeChat and the convenience of WeChat applets provide new ideas for the innovation of blended teaching methods. In order to solve the problem of neglecting students’ learning progress and stagnation points in traditional teaching in colleges and universities, which leads to poor learning effect and unmeasurable blended learning effect, this paper designs a teaching assistant support system based on the WeChat applet development framework for the professional courses of communication students. This achievement first makes full use of WeChat’s convenient use, social display and high customer stickiness, opening up new methods for students to review and interact with teachers after class, and making up for the shortcomings of traditional teaching methods. Secondly, make full use of online learning time, with multiple functions such as homework submission, message interaction, question feedback, etc., so that learning is not limited by time and space. Through the comparative analysis of the blended teaching effect in three semesters and the questionnaire survey of the teaching class, it is found that the WeChat program designed in this paper not only improves the teaching quality of teachers and the learning efficiency of students, but also has certain innovation, popularization and good practical value.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.