  Stellar flares originate from the sudden reconnection of magnetic flux lines
within the star's atmosphere, particularly in the chromosphere. Automated flare
detection enables exploiting vast photometric datasets from missions like
Kepler. Prior methods rely on outlier detection, facing challenges of
complexity, detection accuracy, and scalability. This paper presents FCN4Flare,
a deep learning approach using fully convolutional networks for precise
point-to-point flare prediction regardless of light curve length. Key
innovations include the NaN Mask to handle missing data, dilated convolutions
to preserve local information, and the MaskDice loss to mitigate severe class
imbalance. Experiments demonstrate significantly improved detection performance
over previous models, with a 0.64 Dice coefficient on Kepler data. Applying
FCN4Flare to Kepler and LAMOST, we compile a catalog of 30,285 high-confidence
flares across 1426 stars. Flare energies are estimated and stellar/exoplanet
properties analyzed, identifying pronounced activity for an M-dwarf hosting a
habitable zone planet. This work overcomes limitations of prior flare detection
methods via deep learning, enabling new scientific discoveries through analysis
of photometric time-series data.
