The integration of intelligent higher vocational industry and education is greatly aided by research on fusion assessment algorithms; yet, the issue of incorrect evaluation placement remains. In the assessment challenge of intelligent higher vocational industry and education integration, the standard ant colony algorithm fails miserably. As a result, this study reviews previous work on the topic and suggests future research on an assessment method. In order to minimize interference with the fusion evaluation algorithm study, the indicators are first classified according to the needs of the algorithm and then located using gradient descent theory. The next step is to develop a research strategy for a convolution neural network fusion assessment algorithm using gradient descent theory. After that, the research outcomes of this algorithm will be thoroughly examined. According to the findings of the MATLAB simulations, the convolution neural network outperforms the conventional ant colony method in certain evaluation metrics, such as research influencing factor time and research correctness of the fusion evaluation algorithm.