Wearable sweat collection sensors have attracted wide attention due to their advantages on portability and continuous monitoring. However, the skin direct-contacting of sensor patches usually triggers the skin allergy or adverse reactions by the dyes, biomarkers or sensing chemical substances. Here, we proposed a biomimetic Janus membrane, inspired by the structure of cactus, that could unidirectionally transport and converge sweat to the assigned detection point. Via unidirectional transferring from hydrophobic layer of Janus membrane to the hydrophilic layer, the sweat droplets were enriched to the assigned detection point of the conical hydrophilic pattern by the Laplace Pressure. The bionic osmosis-enrichment sensing patch effectively inhibits direct-contact of indicators to skin, eliminating potential epidermal contamination. This achieved the effect of in-situ perspiration collection under the premise of biosafety isolation. We apply Deep-Learning (DL)-assisted fluorescence sensor to efficiently and accurately detect the concentration of biomarkers in sweat. The convolutional neural network (CNN) model could easily and accurately classify and quantitatively analyze the concentrations of amino acids, Ca 2+ and Cl -, with 100% classification accuracy. The method shown good reliability in collecting and analyzing sweat, and provided a simple index for clinical health monitoring, disease intervention prevention and clinical diagnosis.