IEEE Consumer Electronics Magazine - March/April 2023 - 81

According to the collected actual data of sports
events and the predicted data obtained through
system processing and analysis, draw a statistical
chart of the change trend of the sports event as
shown in Figure 2, and it can be found that the collected
actual data is being processed by the intelligent
sports prediction analysis system. The
predicted value obtained after processing fluctuates
near the curve, but the general trend is consistent.
Moreover, comparing the prediction data, it
is found that this curve fittingmethod has a higher
prediction accuracy for sports events.
CONCLUSION
The following conclusions can be drawn
through this experimental study. Although it can
be seen that data collection is a big project, it has
improved the accuracy to a greater extent. First of
all, the intelligent sports predictive analysis system
effectively improves the accuracy and reliability
of the predictive analysis of sports competition
matters, and at the same time saves the time and
cost of data information collection, processing,
and analysis. Through mobile devices such as
smart phones, you can learn about sports competition-related
information in real time and the
prediction analysis conclusions of experts and
intelligent sports prediction analysis systems on
the competition situation. Second, the particle
swarm optimization algorithm avoids the complicated
information classification and cumbersome
data processing process of traditional sports predictive
analysis methods and uses computer
machine learning algorithms to replace the steps
of human brain predictive analysis to save more
time andmake staff more calmand calm. Resource
scheduling and sports information are released in
an orderly manner. Through the analysis of the
chart, it can be seen that the analysis system
for intelligent sports prediction has improved the
sports hit rate to a certain extent.
Due to the various types of sports competition
information, this article only tests the performance
of the intelligent sports predictive
analysis system from the perspective of system
functions but does not verify the actual application
of the system in more sports information
predictive analysis. Therefore, the intelligent
sports predictive analysis of this research. There
March/April 2023
are still many imperfections in the system
design. In addition, although the research on
sports predictive analysis in this article has
been relatively specific, but limited by the
author's limited understanding of the application
of particle swarm algorithm, there are still
many problems in the technical realization of
system functions. In the future, we hope to continuously
verify and improve the functions of
this system from the predictive analysis of more
types of sports information.
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