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