Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.