With the rapid development of AI technology, AI painting tools are increasingly used in art creation. However, the effects of works created by different users using AI painting tools vary. Finding out the factors that affect the level of art creation after users use AI painting tools is a matter of concern. To solve this problem, this paper proposes a new Multi-Classification Attention Support Vector Machine (MCASVM) with cross-entropy loss function. By identifying and predicting the level of creativity of ordinary users after using AI painting tools, this model compares and analyzes the influencing factors behind the high and low effects of artistic creativity enhancement after using AI painting tools. The main contribution of this paper is to establish the Art Creation Ability Assessment Dataset (ACAAD) through real data collection to provide data support for subsequent assessments. Meanwhile, MCASVM directly handles the multi-classification problem in the established dataset by introducing multiple SVMs. Among other things, the probabilistic calibration network adjusts the model output so that its predicted probabilities are closer to the probability that the sample truly belongs to the classification. DBAM enhances the feature fusion capability of the model by explicitly focusing on the important channel and spatial features, and it enables the model to more accurately recognize and differentiate between changes in the creative abilities of different users before and after using AI painting tools. The experimental results show that the artistic creativity of ordinary users can be enhanced by AI painting tools, where the most central influencing factors are interest level and social support.

Wide-angle images often suffer from severe radial distortion, compromising geometric accuracy and challenging image correction and real-time stitching, especially in resource-constrained embedded environments. To address this, this study proposes a wide-angle image correction and stitching framework based on a Swin Transformer, optimized for lightweight deployment on edge devices. The model integrates multi-scale feature extraction, Thin Plate Spline (TPS) control point prediction, and optical flow-guided constraints, balancing correction accuracy and computational efficiency. Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. To validate practical applicability, the model is deployed on a Jetson TX2 NX device, and a real-time dual-camera stitching system is built using C++ and DeepStream. The system achieves 15 FPS at 1400 × 1400 resolution, with a correction latency of 56 ms and stitching latency of 15 ms, demonstrating efficient hardware utilization and stable performance. This study presents a deployable, scalable, and edge-compatible solution for wide-angle image correction and real-time stitching, offering practical value for applications such as smart surveillance, autonomous driving, and industrial inspection.

Augmented Reality (AR) technology has improved the dissemination of cultural heritage. The existing research predominantly focuses on technical presentation and user experience. However, the underlying mechanisms driving user adoption remain underexplored, especially in craftsmanship-related intangible cultural heritage (ICH) AR games. Specifically, there is a notable gap in the theoretical modeling of the relationship between perceptual factors and cognitive processing pathways. This study integrates the Technology Acceptance Model (TAM) and Cognitive Load Theory (CLT) and develops a conceptual framework linking perceived dimensions, cognitive load, and behavioral intention. The model incorporates four key perceptual constructs—immersion, visual appeal, interactivity, and learning friendliness—and investigates their influence on user adoption behavior. Empirical data were collected from 412 valid questionnaire responses, supplemented by 12 in-depth semi-structured interviews with experienced users. Structural Equation Modeling (SEM) was used to validate the model and test the hypotheses. The results indicate that perceived ease of use and usefulness significantly affect users’ intention to adopt. At the same time, visual appeal and interactivity indirectly influence behavioral intention through reductions in cognitive load and function as a partial mediator. This study contributes to the theoretical extension of TAM and CLT in multi-sensory cultural experience environments and provides actionable design strategies for interface aesthetics, interactive logic, and content structuring in craftsmanship ICH AR games. It supports the effective dissemination and sustainable transmission of intangible cultural heritage in the digital era.

The Ganjiang River–Poyang Lake Basin is a typical river–lake composite water-system region and a major concentration area of intangible cultural heritage (ICH) in Jiangxi Province. However, officially listed ICH does not simply represent the natural distribution of cultural practices but reflects the combined effects of historical accumulation, environmental contexts, and institutional recognition. Based on 616 national- and provincial-level ICH items, this study employs the nearest neighbor index, kernel density analysis, Lorenz curve, standard deviational ellipse, and Geodetector to examine spatial patterns and associated factors. The results reveal significant spatial clustering (NNI = 0.31, Z = −39.16), characterized by riverine concentration, lakeside distribution, and polycentric development. Traditional craftsmanship and folk customs cluster around Poyang Lake, while traditional drama, folk literature, and quyi extend along the Ganjiang River. Distance to major water systems (q = 0.821), policy support (q = 0.813), and inheritors (q = 0.806) show the highest explanatory power. The findings reveal a spatial process of lake-area accumulation, river-channel diffusion, and nodal support.