In order to solve the problems of low prediction accuracy of the traditional rolling force model and the dung beetle optimizer(DBO) algorithm′s tendency to fall into local optimal solutions, a rolling force prediction model based on the improved dung beetle optimisation algorithm combined with the self-attention(SA) mechanism for long and short-term memory(LSTM) networks is proposed. An improved dung beetle optimizer(IDBO) algorithm is obtained by adding the golden sine algorithm and dynamic weight coefficients and introducing the Circle chaotic mapping, and by combining the LSTM network with the SA mechanism, the IDBO-SA-LSTM cold rolling force prediction model is established, and compared with other models. Six different benchmark functions are used for testing, and simulation experiments show that IDBO algorithm has faster convergence speed and optimization accuracy than the sparrow search algorithm, the dung beetle optimization algorithm, the grey wolf search algorithm and so on. The rolling force prediction experiments are carried out using 6554 field operation data of a two-stand cold rolling unit, and the results show that the prediction error indexes of the IDBO-SA-LSTM algorithm are smaller than the other comparative models, and the IDBO-SA-LSTM algorithm can predict the rolling force within ±4% with the hit rate of 99%, with high model accuracy and good generalization ability.