[Retracted] Career Recommendation for College Students Based on Deep Learning and Machine Learning

Algorithm 1 Description of hybrid convolutional neural network training algorithm.

Input: input feature D
Output: hybrid convolutional neural network model
(1)	Initialization parameters: the number of iterations t, the number of convolution channels n, the learning rate of the Adam algorithm, and the hyperparameters , and
(2)	for i = 1 to t do
(3)	for j = 1 to n do//n represents the number of convolution channels
(4)	Calculate the extracted features of the j-th CNN channel model, and the output is
(5)	end for
(6)	Integrate the extracted features of multichannel convolution , get F
(7)	Input the F input convolution and local connection hybrid processing model to obtain the recommended prediction probabilities and , respectively
(8)	Averaging and to get the current prediction result
(9)	Calculate the error between the predicted result and the actual recommended result according to formula (4)
(10)	Calculate the reciprocal of model parameters W and b according to back propagation and chain rule