  One of the primary goals of space-borne gravitational wave detectors is to
detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents
a significant challenge due to the complex and lengthy EMRI signals, further
compounded by their inherently faint nature. In this letter, we introduce a
2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for
space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 %
false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100.
Especially, the key intrinsic parameters of EMRIs such as mass and spin of the
supermassive black hole (SMBH) and the initial eccentricity of the orbit can be
inferred directly by employing a VGG network. The mass and spin of the SMBH can
be determined at 99 % and 92 % respectively. This will greatly reduce the
parameter spaces and computing cost for the following Bayesian parameter
estimation. Our model also has a low dependency on the accuracy of the waveform
model. This study underscores the potential of deep learning methods in EMRI
data analysis, enabling the rapid detection of EMRI signals and efficient
parameter estimation .
