With the deterioration of the global economic situation and the stagnation or regression of the development of enterprises, the problem of college students’ employment and entrepreneurship has been particularly prominent in recent years, and it is also one of the key points that can not be ignored in carrying out economic construction. The article realizes the prediction of college students’ entrepreneurship and employment market trends based on ARIMA-LSTM by designing the ARIMA algorithm model and combining it with the LSTM model architecture, taking the college students’ entrepreneurship and employment data from 2010 to 2022 as the research data, and using two evaluation indexes, namely, the mean absolute percentage error (MAPE) and the root mean square error (RMSE), to predict the results. Evaluation. From the analysis results ARIMA model prediction fit is high. Comparing the prediction results of the combined model with those of the LSTM model and the ARIMA model, the comparison results show that the combined model constructed in this paper can effectively fit the linear and nonlinear intertwined and superimposed trends of the time series compared with a single model, and the relative error of prediction is smaller at 33.78, which makes the results more accurate. The combined model can help the management department related to college students’ employment and entrepreneurship make reasonable decisions and improve efficiency.