  In pulsar astronomy, detecting effective pulsar signals among numerous pulsar
candidates is an important research topic. Starting from space X-ray pulsar
signals, the two-dimensional autocorrelation profile map (2D-APM) feature
modelling method, which utilizes epoch folding of the autocorrelation function
of X-ray signals and expands the time-domain information of the periodic axis,
is proposed. A uniform setting criterion regarding the time resolution of the
periodic axis addresses pulsar signals without any prior information. Compared
with the traditional profile, the model has a strong anti-noise ability, a
greater abundance of information and consistent characteristics. The new
feature is simulated with double Gaussian components, and the characteristic
distribution of the model is revealed to be closely related to the distance
between the double peaks of the profile. Next, a deep convolutional neural
network (DCNN) is built, named Inception-ResNet. According to the order of the
peak separation and number of arriving photons, 30 data sets based on the
Poisson process are simulated to construct the training set, and the
observation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-ray
Timing Explorer (RXTE) are selected to generate the test set. The number of
training sets and the number of test sets are 30,000 and 5,400, respectively.
After achieving convergence stability, more than 99$\%$ of the pulsar signals
are recognized, and more than 99$\%$ of the interference is successfully
rejected, which verifies the high degree of agreement between the network and
the feature model and the high potential of the proposed method in searching
for pulsars.
