  The existing matched filtering method for gravitational wave (GW) search
relies on a template bank. The computational efficiency of this method scales
with the size of the templates within the bank. Higher-order modes and
eccentricity will play an important role when third-generation detectors
operate in the future. In this case, traditional GW search methods will hit
computational limits. To speed up the computational efficiency of GW search, we
propose the utilization of a deep learning (DL) model bank as a substitute for
the template bank. This model bank predicts the latent templates embedded in
the strain data. Combining an envelope extraction network and an astrophysical
origin discrimination network, we realize a novel GW search framework. The
framework can predict the GW signal's matched filtering signal-to-noise ratio
(SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR
holds greater physical interpretability than the $p_{score}$ metric. Moreover,
the intermediate results generated by our approach, including the predicted
template, offer valuable assistance in subsequent GW data processing tasks such
as parameter estimation and source localization. Compared to the traditional
matched filtering method, the proposed method can realize real-time analysis.
The minor improvements in the future, the proposed method may expand to other
scopes of GW search, such as GW emitted by the supernova explosion.
