  The EHT has captured a series of images of black holes. These images could
provide valuable information about the gravitational environment near the event
horizon. However, accurate detection and parameter estimation for candidate
black holes are necessary. This paper explores the potential for identifying
black holes in the ultraviolet band using space telescopes. We establish a data
pipeline for generating simulated observations and present an ensemble neural
network model for black hole detection and parameter estimation. The model
achieves mean average precision [0.5] values of 0.9176 even when reaching the
imaging FWHM ($\theta_c$) and maintains the detection ability until
$0.54\theta_c$. The parameter estimation is also accurate. These results
indicate that our methodology enables super-resolution recognition. Moreover,
the model successfully detects the shadow of M87* from background noise and
other celestial bodies and estimates its inclination and positional angle. Our
work demonstrates the feasibility of detecting black holes in the ultraviolet
band and provides a new method for black hole detection and further parameter
estimation.
