Recent advancements in neuromorphic computing hardware have led to significant progress in image classification, speech recognition, and fuzzy computing, outperforming traditional von Neumann computing paradigm. However, the widely-investigated memristor-based neuromorphic computing hardware still suffers high writing/reading currents and serious variability issue as well as sneak path challenges, leading to high power consumption and peripheral circuit design complication. Memcapacitor-based neuromorphic computing is expected to alleviate these problems, while the limited memory windows and endurance hindered the practical applications. Here, we present a hafnium oxide-based ferroelectric memcapacitor developed through work function engineering. The memcapacitor demonstrates an overall excellent performance in memory windows (∼7.8 fF/μm2), endurance (>109 cycles), retention (>10 years), dynamic energy consumption (31 fJ/inference), and near-zero standby static power consumption. The fabricated memcapacitor array shows high linearity and device-to-device variations, and can perform complete multiplication-accumulation (MAC) operation. The constructed artificial neural network (ANN) achieves 96.68 % accuracy on the MNIST data set after 200 epochs. Our findings underscore the potential of ferroelectric memcapacitor device as a robust candidate for high energy-efficiency neuromorphic computing applications in intelligent terminals.