The efficiency of sensor arrays in parallel discrimination of multianalytes is fundamentally influenced by the quantity and performance of the sensor elements. The advent of combinational design has notably accelerated the generation of chemical libraries, offering numerous candidates for the development of robust sensor arrays. However, screening elements with superior cross-responsiveness remains challenging, impeding the development of high-performance sensor arrays. Herein, we propose a new deep learning-assisted, two-step screening strategy to identify the optimal combination of minimal sensor elements, using a designed volatile organic compounds (VOCs)-targeted sensor library. 400 sensing elements constructed by pairing 20 ionizable cationic elements and 20 anionic dyes in the sensor library were employed for various VOCs, generating plentiful color variation data. By employing a feedforward neural network─random forest-recursive feature elimination (FRR) algorithm, sensing elements were effectively screened, resulting in the rapidly producing 8-element and 10-element arrays for two VOC models, both achieving 100% discrimination accuracy. Furthermore, a smartphone-based point-of-care testing (POCT) platform achieved cancer discrimination in a simulated cancer VOC model, using image-based deep learning, demonstrating the rationality and practicality of deep learning in the assembly of sensor elements for parallel sensing platforms.