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In this work, six convolutional neural networks (CNNs) have been trainedbased on %different feature images and arrays from the database including15,638 superflare candidates on solar-type stars, which are collected from thethree-years observations of Transiting Exoplanet Survey Satellite ({\em TESS}).These networks are used to replace the artificially visual inspection, whichwas a direct way to search for superflares, and exclude false positive eventsin recent years. Unlike other methods, which only used stellar light curves tosearch superflare signals, we try to identify superflares through {\em TESS}pixel-level data with lower risks of mixing false positive events, and givemore reliable identification results for statistical analysis. The evaluatedaccuracy of each network is around 95.57\%. After applying ensemble learning tothese networks, stacking method promotes accuracy to 97.62\% with 100\%classification rate, and voting method promotes accuracy to 99.42\% withrelatively lower classification rate at 92.19\%. We find that superflarecandidates with short duration and low peak amplitude have lower identificationprecision, as their superflare-features are hard to be identified. The databaseincluding 71,732 solar-type stars and 15,638 superflare candidates from {\emTESS} with corresponding feature images and arrays, and trained CNNs in thiswork are public available.

Convolutional Neural Networks for Searching Superflares from Pixel-level Data of the Transiting Exoplanet Survey Satellite