  One of the primary objectives for space-borne gravitational wave detectors is
the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a
substantial challenge because of the complex and long EMRI signals, further
complicated by their inherently faint signal. In this research, we introduce a
2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for
space-borne detectors. Our method employs the Q-transform for data
preprocessing, effectively preserving EMRI signal characteristics while
minimizing data size. By harnessing the robust capabilities of CNNs, we can
reliably distinguish EMRI signals from noise, particularly when the
signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden''
EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure
practical utility. We assess our model's performance using a 0.5-year dataset,
achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive
rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and
1\% FPR at an SNR of 50. This study underscores the promise of incorporating
deep learning methods to advance EMRI data analysis, potentially leading to
rapid EMRI signal detection.
