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Data transmission for Industrial Internet of Things (IIoTs) is of the utmost importance, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. On the one hand, as a key characteristic for Industry 5.0, deterministic transmission aims to ensure the data arrive at destination devices accurately, has attracted remarkable attentions. On the other hand, energy efficiency problem is another major concern in Industry 5.0 since the massive Machine-Type Devices (MTDs). Consequently, in this paper, we investigate the tradeoff between energy efficiency and delay determinacy in energy harvesting-powered Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) communications. In particular, we first derive the probability of the transmission delay falling within a certain time window to characterize the delay determinacy and then figure out the relationship between delay determinacy and the energy efficiency by taking the system control performance into account. After that, in order to model the uncertainty of the stochastic environment, we formulate the problem as a stochastic game by jointly considering transmission power control, frequency spectrum allocation and the selection of base stations. Afterwards, a random graph-based sparse Long Short-Term Memory (LSTM) network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates that the proposed sparse LSTM algorithm enables to achieve a specific delay determinacy with a higher energy efficiency as compared to Deep Q-Learning Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. In addition, we also analysis the influence of sparse coefficient and the size of time window on the relationship between energy efficiency and delay determinacy. [ABSTRACT FROM AUTHOR]