Lobster eye telescopes are ideal monitors to detect X-ray transients because they could observe celestial objects over a wide field of view in the X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point-spread functions, making it hard to design a high-efficiency target detection algorithm. In this paper, we integrate several machine-learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would first generate two 2D images with different pixel scales according to positions of photons on the detector. Then, an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. Finally, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope on board the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mcrab (9.6 × 10−11 erg cm−2 s−1) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.