E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware–algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human–machine interactions, and we explore the current challenges and future development directions.

Conductive hydrogels are a class of multifunctional composites constructed by introducing conductive components into a three‐dimensional polymer network, combining the high water‐content, stretchability, and biocompatibility of traditional hydrogels. In recent years, researchers have developed stimuli‐responsive conductive hydrogels (SRCHs) through molecular functionalization design, which can respond to external stimuli such as mechanical stress, temperature, pH, light, electric field, etc., and realize electrical signal output or mechanical behavior modulation, so as to satisfy the requirements of smart devices for dynamic sensing and active response of materials. Thanks to the synergistic effect of active environmental responsiveness and electrical conductivity, SRCHs show a broad application prospect in smart sensing and actuation. However, due to the complexity of the environment, it is still difficult to utilize SRCHs materials to construct sophisticated smart devices. This paper systematically reviews the progress of SRCHs in material design and smart sensing and actuation applications in the past five years, focuses on their stimuli‐responsive mechanisms and performance optimization strategies, and summarizes the current challenges and future development directions, with a view to providing theoretical references and technological inspirations for the development of next‐generation smart materials.