Efficient and reliable identification of emerging pathogens is crucial for the design and implementation of timely and proportionate control strategies. This is difficult if the pathogen is so far unknown or only distantly related with known pathogens. Diagnostic metagenomics – an undirected, broad and sensitive method for the efficient identification of pathogens – was frequently used for virus and bacteria detection, but seldom applied to parasite identification. Here, metagenomics datasets prepared from swine faeces using an unbiased sample processing approach with RNA serving as starting material were re-analysed with respect to parasite detection. The taxonomic identification tool RIEMS, used for initial detection, provided basic hints on potential pathogens contained in the datasets. The suspected parasites/intestinal protists (Blastocystis, Entamoeba, Iodamoeba, Neobalantidium, Tetratrichomonas) were verified using subsequently applied reference mapping analyses on the base of rRNA sequences. Nearly full-length gene sequences could be extracted from the RNA-derived datasets. In the case of Blastocystis, subtyping was possible with subtype (ST)15 discovered for the first known time in swine faeces. Using RIEMS, some of the suspected candidates turned out to be false-positives caused by the poor status of sequences in publicly available databases. Altogether, 11 different species/STs of parasites/intestinal protists were detected in 34 out of 41 datasets extracted from metagenomics data. The approach operates without any primer bias that typically hampers the analysis of amplicon-based approaches, and allows the detection and taxonomic classification including subtyping of protist and metazoan endobionts (parasites, commensals or mutualists) based on an abundant biomarker, the 18S rRNA. The generic nature of the approach also allows evaluation of interdependencies that induce mutualistic or pathogenic effects that are often not clear for many intestinal protists and perhaps other parasites. Thus, metagenomics has the potential for generic pathogen identification beyond the characterisation of viruses and bacteria when starting from RNA instead of DNA.