The use of fiber optics in computer networks improves the data handling rate and aids in high-level real-time application support for different user categories. Design and modeling of optical communications for computer networks requires difficult slicing and connectivity process for preventing signal losses. In this article, a Connection-Adjustable Network Slicing (CANS) process is introduced to prevent signal losses due to heterogeneous application support. The proposed process identifies service demands and the actual network transmit capacity for acknowledging services. The optical features are improved using the recommended learning preferences in order to achieve high service delivery. In the amplification process, the infrastructure support and slicing delays are accounted for preventing signal losses. To improve network stability with low-level computer networks, the service-to-loss forecast is predicted using recommendation learning. Therefore, the proposed process’s performance is validated using the metrics service latency, slicing rate, service sharing ratio, and outage.