With the continuous expansion of data center scale， Power Usage Effectiveness （PUE） has become an important indicator for measuring energy efficiency of data centers. Most existing research focuses on hyperscale data centers， while small air-cooled data centers have significant differences in scalability， load fluctuations， network infrastructure， and management approaches， leading to relatively insufficient related prediction studies. Therefore， a PUE prediction model of small air-cooled data centers based on LSTM-Transformer architecture was proposed. The model was designed to combine the capability of Long Short-Term Memory （LSTM） network in extracting time-series features with the advantage of Transformer in modelling global dependencies， so that multi-source heterogeneous energy data were integrated effectively， thereby enhancing the characterization and prediction capabilities of PUE variation patterns. Experimental results on a real data center dataset show that the Mean Squared Error （MSE）， Mean Absolute Error （MAE）， and Coefficient of Determination （R2） of the proposed model are 0.000 7， 0.021 4， and 0.958 3， respectively； compared with the LSTM model， the proposed model has the MSE and MAE reduced by 53.33% and 28.43%， respectively， and R2 increased by 0.051 6； compared with the Transformer model， the proposed model has the MSE and MAE reduced by 78.79% and 45.41%， respectively， and R2 increased by 0.136 3， verifying the effectiveness of the proposed hybrid architecture in improving PUE prediction accuracy. It can be seen that the proposed model can provide reliable support for the energy efficiency optimization and operation management of small air-cooled data centers.