Research on the Evaluation System of Talent Innovation Capability in Unmanned System Engineering Based on CABC

In response to the lack of quantitative evaluation methods for the innovation ability of unmanned system engineering in military academies, a BP neural network is used to establish an evaluation index system for students' innovation ability based on educational data, and the membership functions of each index are given. Chaos theory is introduced to improve the local search mechanism of the bee colony algorithm, so as to obtain stronger global optimization ability; Then, the connection weight and threshold of BP neural network are optimized through chaotic bee colony algorithm (CABC), which weakens the influence of subject differences in the evaluation process, and is conducive to obtaining objective evaluation conclusions. It can also effectively solve the problems of classic ABC and BP networks, such as easily trapped local extremum, slow Rate of convergence, etc. Finally, experimental data was used to validate the effectiveness of this method in evaluating the innovative ability of intelligent personnel in military academies.